Note: Intro -- Preface -- Organization -- Contents -- Full Papers -- Implementation of Real Data for Financial Market Simulation Using Clustering, Deep Learning, and Artificial Financial Market -- 1 Introduction -- 2 Related Works -- 3 Data and Extracting HFT-MM Orders -- 4 Trader Models -- 4.1 Stylized Trader Model -- 4.2 Traditional HFT-MM Trader Model -- 4.3 HFT-MM Machine Learned (ML) Trader Model -- 5 Simulations -- 6 Results -- 6.1 Comparison of Ordering Price Distribution -- 6.2 Comparison by Kullback-Leibler Divergence -- 6.3 Deep Analysis for ML Model Simulation -- 7 Discussion -- 8 Conclusion -- References -- Hybrid Dynamic Programming for Simultaneous Coalition Structure Generation and Assignment -- 1 Introduction -- 2 Related Work -- 3 Basic Concepts and Notation -- 4 The Dynamic Programming Algorithm -- 5 The Hybrid Algorithm -- 6 Benchmarks and Experiments -- 6.1 Optimality Benchmarks -- 6.2 Anytime Benchmarks -- 7 Conclusions -- References -- A Socio-psychological Approach to Simulate Trust and Reputation in Modal Choices -- 1 Introduction -- 2 Related Work -- 2.1 Learning Models -- 2.2 Reputation Models -- 2.3 Socio-cognitive Models -- 3 Agent Decision-Making Architecture -- 3.1 Modelling Trust and Reputation -- 3.2 Utility Function -- 3.3 An Overview -- 4 Experiment -- 4.1 The Behaviour-Driven Demand Model (BedDeM) -- 4.2 Setup -- 4.3 Results -- 4.4 Discussion -- 5 Conclusion -- References -- Reasoning About Trustworthiness in Cyber-Physical Systems Using Ontology-Based Representation and ASP -- 1 Introduction -- 2 Background -- 2.1 CPS Framework (CPSF), CPS Ontology, and Representation -- 2.2 Answer Set Programming -- 3 OA4cps: A Hybrid Reasoner for CPS -- 3.1 From CPS Theory Specification to ASP Encoding -- 3.2 Queries Related to Trustworthiness -- 3.3 Queries Answering Using (,n) -- 4 Towards a Decision-Support System for CPSF. , 5 Conclusions, Related Work, and Discussion -- References -- Optimal Deterministic Time-Based Policy in Automated Negotiation -- 1 Introduction -- 2 Notation and Problem Statement -- 2.1 Policy -- 2.2 Acceptance Models -- 2.3 Problem Statement -- 3 Related Work: GCA -- 4 Policy Operations -- 5 Proposed Algorithms -- 5.1 Static Acceptance Models: QGCA -- 5.2 General Time Dependent Acceptance Models: PA -- 5.3 Extensions -- 6 Evaluation -- 6.1 Probabilistic Opponents on Random Domains -- 6.2 Realistic Domains and Opponents -- 7 Limitations and Discussion -- 8 Conclusions -- References -- Agent Simulation of Collision Avoidance Based on Meta-strategy Model -- 1 Introduction -- 2 Background -- 3 Active and Passive Strategy Acquisition Experiments -- 3.1 Methods -- 3.2 Results -- 4 Experiment of Cooperative Behavior Acquisition Using Meta-strategy -- 4.1 Methods -- 4.2 Results -- 5 Discussion -- 6 Conclusion -- References -- The Smart Appliance Scheduling Problem: A Bayesian Optimization Approach -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Constraint Satisfaction Problems (CSPs) -- 3.2 Bayesian Optimization -- 4 The Smart Appliance Scheduling (SAS) Problem with Users' Satisfaction -- 4.1 The User's Satisfaction Function -- 5 Bayesian Optimization for the SAS Problem -- 5.1 Existing Acquisition Functions -- 5.2 Acquisition Functions for the SAS Problem -- 6 Empirical Evaluations -- 6.1 Experimental Setup -- 6.2 Impact of Energy-Cost Based Acquisition Functions -- 6.3 Impact of Different Kernel Functions -- 7 Conclusions -- References -- Distance-Based Heuristic Solvers for Cooperative Path Planning with Heterogeneous Agents -- 1 Introduction -- 2 Preliminary -- 2.1 Graphs and Requests -- 2.2 Vehicle Platooning Problem -- 2.3 Cooperative Path Planning Problem -- 2.4 Exact Solvers Using Integer Programming -- 3 Distance-Based Heuristic Solver. , 3.1 High-Level Overview -- 3.2 Step 1. Evaluating Benefits of Cooperation for Vehicle Pairs -- 3.3 Step 2. Finding Vehicle Groups by Assignments -- 3.4 Step 3. Constructing Routes -- 4 Computational Experiments -- 4.1 Evaluation of IP-Based Exact Solver -- 4.2 Evaluations of Gap Parameter in IP-Solver -- 4.3 Experiments Using Heuristics -- 4.4 Summary of Experiments -- 5 Related Work -- 6 Conclusion -- References -- Policy Advisory Module for Exploration Hindrance Problem in Multi-agent Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 3.1 Dec-POMDP -- 3.2 Agents and Sequential Tasks -- 3.3 Rewards Setting -- 3.4 Description of Exploration Hindrance Problem -- 4 Proposed Method -- 4.1 Learning Architecture for Agents in Sequential Tasks -- 4.2 Policy Advisory Module -- 5 Experimental Evaluation -- 5.1 Experiment Settings -- 5.2 Network Structure in Agent -- 5.3 Experiment 1-Training Results -- 5.4 Ratio of Incompletion -- 5.5 Effect of Policy Advisory Module -- 5.6 Number of Collisions -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Analysis of Coordination Structures of Partially Observing Cooperative Agents by Multi-agent Deep Q-Learning -- 1 Introduction -- 2 Related Studies -- 3 Problem Formulation -- 3.1 Problem and Environment -- 3.2 View Obstruction -- 4 Proposed Method -- 4.1 Agent View Methods -- 4.2 Local View -- 4.3 Relative View -- 4.4 Merged View -- 4.5 Neural Network Structure -- 5 Experiment and Discussion -- 5.1 Experimental Setting -- 5.2 Experiment 1: Static Spawn Location -- 5.3 Experiment 2: Dynamic Spawn Location -- 5.4 Discussion -- 6 Conclusion -- References -- Policy Adaptive Multi-agent Deep Deterministic Policy Gradient -- 1 Introduction -- 2 Related Work -- 2.1 Centralized Critic -- 2.2 Decentralized Learning -- 2.3 Opponent Modeling -- 2.4 Meta-learning. , 2.5 Communication -- 3 Background -- 3.1 Partially Observable Markov Games -- 3.2 Multi-agent Deep Deterministic Policy Gradient -- 3.3 Dealing Non-stationarity in MADDPG -- 4 Policy Adaptive MADDPG -- 4.1 Learning Multiple Policies -- 4.2 Learning Policy Predictors -- 5 Experiments -- 5.1 Environments -- 5.2 Setup -- 5.3 Results -- 6 Conclusion -- References -- Multi-agent Planning with High-Level Human Guidance -- 1 Introduction -- 2 Related Work -- 3 The HL-DEC-POMDP Model -- 4 Solving HL-DEC-POMDPs -- 4.1 The Command Model -- 4.2 Point-Based Policy Optimization -- 4.3 Suggesting Commands to the Operators -- 5 Experiments -- 6 Conclusions -- A The Benchmark Problems -- A.1 Meeting in a 33 Grid -- A.2 Cooperative Box-Pushing -- References -- Preference Elicitation in Assumption-Based Argumentation -- 1 Introduction -- 2 Background -- 3 Approach for Preference Elicitation in ABA -- 4 Demonstration (Example) -- 5 Related Work -- 6 Conclusions and Future Work -- References -- Declarative Preferences in Reactive BDI Agents -- 1 Introduction -- 2 Background -- 3 Method -- 3.1 Components of Extended Agent Programs -- 3.2 Transformation to Logic Program -- 3.3 Plan Priority Extraction and Script Rewriting -- 4 Application -- 5 Discussion and Further Developments -- References -- Predicting the Priority of Social Situations for Personal Assistant Agents -- 1 Introduction -- 2 Proposed Approach -- 3 Concepts and Methods -- 3.1 Social Science Concepts -- 3.2 Machine Learning Methods -- 4 Crowd-Sourcing User Study -- 4.1 Choice of Concepts -- 4.2 Method -- 4.3 Description of Data -- 5 Predicting Priority of Social Situations -- 5.1 Predictive Models and Results -- 5.2 Determining Important Features for Predictions -- 5.3 Role of Personal Values in Predicting Priorities -- 6 Conclusions -- 6.1 Contributions -- 6.2 Limitations and Future Work -- References. , Mutex Propagation for SAT-based Multi-agent Path Finding -- 1 Introduction -- 2 Background -- 2.1 Mutex Propagation in MAPF -- 2.2 Conflict-Based Search with Mutex Propagation -- 3 SAT-Based Approach: MDD-SAT -- 3.1 Mutexes in SAT-Based Solver -- 3.2 Experimental Evaluation -- 4 Conclusion -- References -- A SMT-based Implementation for Safety Checking of Parameterized Multi-Agent Systems -- 1 Introduction -- 2 Related Work and Contribution -- 3 PMASs: Parameterized MAS -- 3.1 Agent Formulae -- 3.2 Concurrent and Interleaved PMASs -- 4 A Practical Solution to the Reachability Problem for PMASs -- 4.1 MCMT: Model Checker Modulo Theories -- 4.2 MCMT Input Files for Interleaved and Concurrent PMASs -- 4.3 SAFE: the Swarm Safety Detector -- 5 Execution of SAFE-MCMT -- 5.1 A Further Example -- 6 Conclusions and Future Work -- References -- A Goal-Based Framework for Supporting Medical Assistance: The Case of Chronic Diseases -- 1 Introduction and Motivations -- 2 Preliminaries -- 3 Working Example -- 4 Proposed Framework -- 4.1 The Proposed Extension -- 4.2 Next Question Selection -- 5 The Framework in Action -- 6 Evaluation -- 7 Final Remarks -- References -- Optimal Control of Pedestrian Flows by Congestion Forecasts Satisfying User Equilibrium Conditions -- 1 Introduction -- 1.1 Related Work -- 2 Simulation Model and Problem Formulation -- 2.1 Simulation Model -- 2.2 Parameters -- 2.3 Problem Formulation -- 3 Algorithm -- 3.1 Finding Congestion Forecast -- 3.2 Optimizing the Proportion of Visitors Receiving Congestion Information -- 4 Computational Experiments -- 4.1 Experimental Settings -- 4.2 Comparison with Black-Box Optimization Methods -- 4.3 Optimization in Large-Scale Simulation -- 4.4 Scalability Analysis -- 4.5 Convergence Analysis -- 5 Conclusion -- References -- Short Papers. , Automated Negotiation Mechanism and Strategy for Compensational Vehicular Platooning.

Note: Intro -- Preface -- Contents -- Contributors -- Part I Agent-Based Complex Automated Negotiations -- Chapter 1: Intra-Team Strategies for Teams Negotiating Against Competitor, Matchers, and Conceders -- 1.1 Introduction -- 1.2 General Framework -- 1.3 Intra-Team Strategies -- 1.3.1 Representative (RE) -- 1.3.2 Similarity Simple Voting (SSV) -- 1.3.3 Similarity Borda Voting (SBV) -- 1.3.4 Full Unanimity Mediated (FUM) -- 1.4 Implementation in Genius -- 1.5 Experiments and Results -- 1.5.1 ANAC 2010 Agents -- 1.5.2 Test Domain: Hotel Group Booking -- 1.5.3 Experimental Setting -- 1.5.4 Results -- 1.6 Related Work -- 1.7 Conclusions and Future Work -- References -- Chapter 2: Alternative Social Welfare Definitions for Multiparty Negotiation Protocols -- 2.1 Introduction -- 2.2 The Negotiation Protocol -- 2.2.1 Basic Operation of the Negotiation Protocol -- 2.3 Agents' Local Exploration (GPS) -- 2.4 The Mediation Mechanisms -- 2.4.1 Forming Clusters of Agents (HC) -- 2.4.2 Computing the Feedback Contract -- 2.4.2.1 OWA Operators -- 2.4.2.2 Quantifier Guided Aggregation -- 2.4.2.3 Computation of the Feedback Contract -- 2.4.3 Measuring the Quality of the Agreement -- 2.5 Experimental Evaluation -- 2.6 Conclusion -- References -- Chapter 3: Multilateral Mediated Negotiation Protocols with Feedback -- 3.1 Introduction -- 3.2 Mediated Negotiation -- 3.3 Proposed Mediated Negotiation -- 3.3.1 Feedback Based Preference Modeling -- 3.3.2 Feedback Based Protocol -- 3.3.3 Feedback and Voting Based Protocol -- 3.4 Experiments -- 3.4.1 Experimental Setup -- 3.4.2 Results -- 3.5 Discussion -- References -- Chapter 4: Decoupling Negotiating Agents to Explore the Space of Negotiation Strategies -- 4.1 Introduction -- 4.2 Related Work -- 4.2.1 Architecture of Negotiation Strategies -- 4.2.2 Components of Negotiation Strategy. , 4.2.3 Negotiation Strategy Space Exploration -- 4.3 The BOA Agent Architecture -- 4.3.1 Negotiation Environment -- 4.3.2 The BOA Agent -- 4.3.3 Employing the BOA Architecture -- 4.4 Decoupling Existing Agents -- 4.4.1 Identifying the Components -- 4.4.2 Testing Equivalence of BOA Agents -- 4.4.2.1 Identical Behavior Test -- 4.4.2.2 Similar Performance Test -- 4.5 Applications of the BOA Architecture -- 4.5.1 Scaling the Negotiation Space -- 4.5.2 Improving the State of the Art -- 4.5.2.1 Searching the Negotiation Space -- 4.5.2.2 Experimental Results -- 4.6 Conclusion and Future Work -- References -- Chapter 5: A Dynamic, Optimal Approach for Multi-Issue Negotiation Under Time Constraints -- 5.1 Introduction -- 5.2 Historical-Offer Regression -- 5.2.1 Simple Behaviours Regression -- 5.2.2 Complex Behaviours Prediction -- 5.3 Preference Prediction -- 5.4 Optimal Offer Generation -- 5.4.1 A Geometric Method -- 5.4.1.1 Line A and Line B Are not Parallel -- 5.4.1.2 Line A and Line B Are Parallel -- 5.4.1.3 Line A and Line B Are Identical -- 5.4.2 An Algebraic Method -- 5.5 Experiment -- 5.5.1 Experimental Setup -- 5.5.2 Experimental Results -- 5.5.3 Case Study -- 5.6 Conclusion -- References -- Chapter 6: On Dynamic Negotiation Strategy for Concurrent Negotiation over Distinct Objects -- 6.1 Introduction -- 6.2 Related Work -- 6.2.1 Negotiation Model -- 6.2.2 Coordination Approach -- 6.3 Experiments -- 6.3.1 Settings -- 6.3.2 Hypotheses -- 6.3.3 Results and Discussions -- 6.4 Conclusions and Future Work -- References -- Chapter 7: Reducing the Complexity of Negotiations Over Interdependent Issues -- 7.1 Multiple Interdependent Issues -- 7.2 Grouping Contracts and Bidding Based Deal Identification -- 7.3 Subset Rule -- 7.4 Experimental Evaluation -- 7.4.1 Experiment Settings -- 7.4.1.1 Ran -- 7.4.1.2 Subset Rule Based -- 7.4.2 Experimental Results. , 7.5 Case Study -- 7.5.1 Applying the Subset Rule -- 7.5.2 Desirable and Undesirable Effects -- 7.5.3 Exploring the Use of Monetary Values as Weights For Constraints -- 7.6 Conclusion and Future Works -- References -- Chapter 8: Evaluation of the Reputation Network Using Realistic Distance Between Facebook Data -- 8.1 Introduction -- 8.2 Related Work -- 8.2.1 Reputation Mechanism -- 8.2.2 Ranking Techniques for Web Pages -- 8.2.3 HITS Algorithm -- 8.2.4 PageRank -- 8.3 Proposal of a Reputation Network Using Distance Between Users -- 8.3.1 Concept of Realistic Distance Between Users -- 8.3.2 Distance-HITS -- 8.3.3 Distance-PageRank -- 8.4 Parameter Setup Items -- 8.5 Experimental Results -- 8.6 Discussion -- 8.7 Conclusion -- References -- Part II Automated Negotiating Agents Competition -- Chapter 9: An Overview of the Results and Insights from the Third Automated Negotiating Agents Competition (ANAC2012) -- 9.1 Introduction -- 9.2 Set-Up of the Competition -- 9.2.1 New Feature of the 2012 Competition -- 9.2.2 Negotiation Domains -- 9.2.2.1 Qualifying Round -- 9.2.2.2 Final Round -- 9.3 Competition Results -- 9.3.1 Qualifying Round -- 9.3.2 Final Round -- 9.3.3 Results for Specific Domains -- 9.3.4 Social Welfare Achieved by Each Agent -- 9.4 Conclusions and Future Extensions of ANAC -- References -- Chapter 10: An Adaptive Negotiation Strategy for Real-Time Bilateral Negotiations -- 10.1 Introduction -- 10.2 Negotiation Strategy -- 10.2.1 Introduction to the BOA Framework -- 10.2.2 Implementing the BOA Components -- 10.2.2.1 Bidding Strategy -- 10.2.2.2 Opponent Model -- 10.2.2.3 Acceptance Strategy -- 10.3 Empirical Evaluation -- 10.3.1 Experimental Setup -- 10.3.2 Experimental Results -- 10.4 Conclusion and Future Work -- References -- Chapter 11: CUHKAgent: An Adaptive Negotiation Strategy for Bilateral Negotiations over Multiple Items. , 11.1 Introduction -- 11.2 Designing Issues -- 11.2.1 Learning the Opponent's Decision Function or Not? -- 11.2.2 How to Make Concessions to the Opponent? -- 11.2.3 How to Guess the Opponent's Preference? -- 11.3 Strategy Description -- 11.3.1 How to Determine the Acceptance Threshold -- 11.3.2 How to Propose Bids to the Opponent -- 11.4 Conclusion -- References -- Chapter 12: AgentMR: Concession Strategy Based on Heuristic for Automated Negotiating Agents -- 12.1 Introduction -- 12.2 An Implementation of Negotiating Agents Based on Heuristic Strategy -- 12.2.1 Method of Searching for Bid -- 12.2.2 Evaluating Characteristics of Opponent -- 12.2.3 Control of Concession -- 12.3 Conclusion -- References -- Chapter 13: OMAC: A Discrete Wavelet Transformation Based Negotiation Agent -- 13.1 Introduction -- 13.2 Negotiation Environment -- 13.3 Overview of OMAC -- 13.4 Opponent Modeling -- 13.5 Adaptive Adjustment of Concession Rate -- 13.6 Response Mechanism -- 13.7 Conclusions and Future work -- References -- Chapter 14: The Simple-Meta Agent -- 14.1 Introduction -- 14.2 Definitions -- 14.2.1 Constructing Domain Features -- 14.2.2 The Simple Meta-Agent -- References -- Index.

Note: Intro -- Preface -- Organization -- Contents - Part III -- Recommender System -- Mixture of Graph Enhanced Expert Networks for Multi-task Recommendation -- 1 Introduction -- 2 The Proposed Method -- 2.1 Deep Interaction Context Exploitation with Multi-channel Graph Neural Network -- 2.2 Graph Enhanced Expert Network -- 2.3 Model Learning -- 2.4 Discussion -- 3 Experiments -- 3.1 Performance Comparison (RQ1) -- 3.2 Effect of the MGNN Module -- 3.3 Study of MoGENet -- 4 Conclusion -- References -- MF-TagRec: Multi-feature Fused Tag Recommendation for GitHub -- 1 Introduction -- 2 Related Work -- 2.1 Tag Recommendation -- 2.2 Tag Recommendation in Open-Source Communities -- 3 Method -- 3.1 Problem Formulation -- 3.2 Overview of MF-TagRec -- 3.3 CNN for Tag Prediction -- 3.4 Network Training Process -- 4 Experiments -- 4.1 Experimental Dataset -- 4.2 Evaluation Metrics -- 4.3 Experimental Settings -- 4.4 Experimental Results -- 5 Conclusions and Future Work -- References -- Co-contrastive Learning for Multi-behavior Recommendation -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 Interactive View Encoder -- 3.2 Fold View Encoder -- 3.3 Divergence Constraint -- 3.4 Co-contrastive Learning -- 3.5 Efficient Joint Learning Without Sampling -- 4 Experiments -- 4.1 Datasets -- 4.2 Compared Models -- 4.3 Experimental Settings -- 4.4 Performance Comparison -- 4.5 Effectiveness Analysis on Data Sparsity Issue -- 4.6 Ablation Study -- 4.7 Parameter Sensitivity Analysis -- 5 Conclusion -- References -- Pattern Matching and Information-Aware Between Reviews and Ratings for Recommendation -- 1 Introduction -- 2 Related Work -- 3 The Proposed MIAN Model -- 3.1 Global Matching Module -- 3.2 Specific Matching Module -- 3.3 Information-Aware Layer -- 3.4 Interaction Aggregation Layer -- 3.5 Joint Learning of Review Matching and Rating Prediction. , 4 Experiments -- 4.1 Experimental Settings -- 4.2 Overall Performance (RQ1) -- 4.3 Ablation Experimental Study (RQ2) -- 4.4 Case Study (RQ3) -- 5 Conclusion -- References -- Cross-View Contrastive Learning for Knowledge-Aware Session-Based Recommendation -- 1 Introduction -- 2 Notations and Problem Statement -- 3 Method -- 3.1 View Generation -- 3.2 Dual-channel Graph View Encoder -- 3.3 Contrastive Learning and Recommendation -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Over Performance -- 4.3 Model Ablation Study -- 4.4 Handling Different Session Lengths -- 4.5 Hyperparameter Study -- 5 Conclusion -- References -- Reinforcement Learning -- HiSA: Facilitating Efficient Multi-Agent Coordination and Cooperation by Hierarchical Policy with Shared Attention -- 1 Introduction -- 2 Preliminaries and Related Work -- 2.1 Communicative Decentralized Partially Observable Markov Decision Process -- 2.2 Communicative Methods in MAS -- 2.3 Attention Mechanism -- 2.4 Hierarchical Policy with Attention -- 3 Method -- 3.1 Shared Attention Map for Communication -- 3.2 Hierarchical Structure with Shared Attention Mechanism -- 3.3 HiSA for Multi-agent Reinforcement Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experiments on the StarCraft Multi-Agent Challenge -- 4.3 Experiments on the Overcooked -- 5 Summary and Outlook -- References -- DDMA: Discrepancy-Driven Multi-agent Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Partially Observable Stochastic Game -- 3.2 Reinforcement Learning -- 4 Method -- 4.1 Initialization of the Multi-agent Policy -- 4.2 Focused Learning of the Multi-agent Policy -- 4.3 Training -- 5 Experiments -- 5.1 Collision Corridor -- 5.2 MPE Scenarios -- 5.3 Ablation Study -- 6 Conclusion -- References -- PRAG: Periodic Regularized Action Gradient for Efficient Continuous Control. , 1 Introduction -- 2 Background -- 3 From TD-Error to Action Gradient Error -- 3.1 TD-Error and TD-Learning -- 3.2 Action Gradient Error and Action Gradient Regularizer -- 3.3 Periodic Regularized Action Gradient Algorithm -- 4 Experiment -- 4.1 Overall Performance -- 4.2 Ablation Study -- 4.3 Parameter Study -- 5 Related Work -- 6 Conclusion -- References -- Identifying Multiple Influential Nodes for Complex Networks Based on Multi-agent Deep Reinforcement Learning -- 1 Introduction -- 2 Problem Formulation -- 3 Multi-agent Identification Framework -- 3.1 General Framework of MAIF -- 3.2 Framework Elements -- 3.3 Independent Actor-Critic Model -- 3.4 Counterfactual Multi-agent (COMA) Policy Gradients and Gate Recurrent Unit (GRU) Network -- 4 Experimental Preliminaries -- 4.1 Dataset -- 4.2 Comparison Method -- 4.3 Susceptible-Infected-Recovered (SIR) Model -- 5 Experimental Results -- 6 Conclusion -- References -- Online Learning in Iterated Prisoner's Dilemma to Mimic Human Behavior -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Experimental Setup -- 4.1 Iterated Prisoner's Dilemma (IPD) -- 4.2 Behavioral Cloning with Demonstration Rewards -- 4.3 Online Learning Agents -- 5 Results: Algorithms' Tournament -- 5.1 Multi-agent Tournament -- 6 Behavioral Cloning with Human Data -- 7 Clinical Evidences and Implications -- 8 Discussion -- 9 Conclusion -- References -- Optimizing Exploration-Exploitation Trade-off in Continuous Action Spaces via Q-ensemble -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Proposed Method -- 4.1 Ensemble-Based Exploration Strategy -- 4.2 Selective Repeat Update -- 5 Experiments -- 5.1 Parameter Settings -- 5.2 Comparative Evaluation -- 5.3 Ablation Study -- 6 Conclusion -- References -- Hidden Information General Game Playing with Deep Learning and Search -- 1 Introduction -- 2 Background. , 2.1 General Game Playing -- 2.2 Generalised AlphaZero -- 2.3 Recursive Belief-Based Learning -- 3 Method -- 3.1 Propositional Networks for GDL-II -- 3.2 Sampling GDL-II States -- 3.3 CFR Search -- 3.4 Reinforcement Learning -- 4 Experiments -- 4.1 Evaluation Methodology -- 4.2 Results and Discussion -- 5 Conclusion and Future Work -- References -- Sequential Decision Making with ``Sequential Information'' in Deep Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Deep Reinforcement Learning -- 3.2 Depthwise Separable Convolution -- 3.3 3D Temporal Convolution -- 4 Temporal Aggregation Network in DRL -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Results Analysis -- 6 Conclusion and Future Work -- References -- Two-Stream Communication-Efficient Federated Pruning Network -- 1 Introduction -- 2 Proposed Method -- 2.1 Preliminary -- 2.2 Downstream Compression via DRL Agent -- 2.3 Upstream Compression Based on Proximal Operator -- 3 Experimental Setup and Results -- 3.1 Compared Methods -- 3.2 Datasets and Simulation Settings -- 3.3 Experiment Results -- 3.4 Ablation Study -- 4 Conclusion -- References -- Strong General AI -- Multi-scale Lightweight Neural Network for Real-Time Object Detection -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Network Architecture -- 3.2 Fast Down-Sampling Module -- 3.3 Reduced Computational Block -- 3.4 Detection Part -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Hyperspectral Image Classification Based on Transformer and Generative Adversarial Network -- 1 Introduction -- 2 Related Works -- 2.1 Superpixelwise PCA -- 2.2 Auxiliary Classifier GANs -- 3 Proposed Method -- 3.1 The Framework of the Proposed SPCA-TransGAN -- 3.2 The Network Framework of Generator. , 3.3 The Network Framework of Multi-scale Discriminator -- 4 Experimental Results and Analysis -- 4.1 DataSets -- 4.2 Classification Results on Two Data Sets -- 5 Conclusion -- References -- Deliberation Selector for Knowledge-Grounded Conversation Generation -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Problem Statement -- 3.2 Model Description -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Baselines -- 4.4 Supplementary Details -- 4.5 Experimental Results -- 4.6 Ablation Test -- 4.7 Case Study -- 5 Conclusion -- References -- Training a Lightweight ViT Network for Image Retrieval -- 1 Introduction -- 2 Methodology -- 2.1 Knowledge Distillation with Relaxed Contrastive Loss -- 2.2 Quantized Heterogeneous Knowledge Distillation -- 2.3 Distillation Heterogeneous Quantization for Multi-exit Networks -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Comprehensive Comparison Results -- 3.3 Analysis of Distillation Quantization of Multi-exit Networks -- 4 Conclusions -- References -- Vision and Perception -- Segmented-Original Image Pairs to Facilitate Feature Extraction in Deep Learning Models -- 1 Introduction -- 2 Method -- 2.1 Datasets -- 2.2 Segmented-Original Image Pair Training Method -- 3 Experiments -- 3.1 Segmentation Algorithm -- 3.2 Classification Tasks -- 3.3 Unsupervised Learning Tasks -- 4 Conclusion -- References -- FusionSeg: Motion Segmentation by Jointly Exploiting Frames and Events -- 1 Introduction -- 2 Related Work -- 2.1 Motion Segmentation -- 2.2 Visual Transformer -- 3 Methodology -- 3.1 Input Representation -- 3.2 Network Architecture -- 3.3 Feature Fusion Method -- 3.4 Multi-Object Association -- 3.5 Feature Matching and Propagation -- 4 Experiment and Results -- 4.1 Implementation Details -- 4.2 Overview of Datasets -- 4.3 Discussion of Results -- 5 Conclusions and Future Work -- References. , Weakly-Supervised Temporal Action Localization with Multi-Head Cross-Modal Attention.

Note: Intro -- Preface -- Organization -- Contents -- Estimating Difficulty Score of Visual Search in Images for Semi-supervised Object Detection -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Kendall RankLoss -- 3.2 Multi-loss Alternative Strategy -- 4 Experiments -- 4.1 Model for Estimating Difficulty Score -- 4.2 Application of Estimating Difficulty Score -- 5 Conclusion -- References -- Improving Named Entity Recognition with Commonsense Knowledge Pre-training -- 1 Introduction -- 2 Related Work -- 3 Background on Some Supervised Machine Learning Models -- 3.1 The Long Short-Term Memory Model -- 3.2 Bidirectional LSTM -- 3.3 CRF -- 4 The Proposed Approach -- 4.1 Our Proposed NER Model -- 4.2 Input Embeddings -- 5 Experiments and Evaluations -- 5.1 Dataset -- 5.2 Training -- 5.3 Evaluations and Results -- 6 Conclusion -- References -- Neurofeedback and AI for Analyzing Child Temperament and Attention Levels -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Research Method -- 4 Result Analysis -- 5 Conclusions -- Acknowledgement -- References -- Finding Diachronic Objects of Drifting Descriptions by Similar Mentions -- 1 Introduction -- 2 Related Work -- 3 Finding Diachronic Objects -- 3.1 Overview -- 3.2 Similar Mention Ratio -- 4 Experiments -- 4.1 Target Document Sets -- 4.2 Results -- 5 Evaluation and Analysis -- 5.1 Distribution of Word Similarity -- 5.2 Appearance of the Words -- 5.3 Ratio of Drift Description Documents -- 6 Discussion -- 7 Conclusions -- References -- A Max-Min Conflict Algorithm for the Stable Marriage Problem -- 1 Introduction -- 2 Related Work -- 3 Max-Min Conflict Algorithm -- 4 Experiments -- 5 Conclusions -- References -- Empirical Evaluation of Deep Learning-Based Travel Time Prediction -- 1 Introduction -- 2 Related Works -- 3 Deep Learning-Based Traffic Prediction Framework. , 3.1 Data Collection Module -- 3.2 Data Pre-processing Module -- 3.3 The Travel Time Prediction Module -- 4 Experiment Results and Discussion -- 5 Conclusion and Future Work -- References -- Marine Vertebrate Predator Detection and Recognition in Underwater Videos by Region Convolutional Neural Network -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Predator Detection Using Faster-RCNN -- 3.2 Multiple Convolutional and Pooling -- 3.3 Anchor Generation -- 3.4 Region Proposal Network -- 3.5 ROI Pooling -- 4 Evaluation and Results -- 5 Discussion and Conclusions -- References -- Constructing Dataset Based on Concept Hierarchy for Evaluating Word Vectors Learned from Multisense Words -- 1 Introduction -- 2 Related Work -- 2.1 Word Embedding Methods -- 2.2 Word Similarity Dataset -- 2.3 Lexical Database with Concept Hierarchy -- 3 Proposed Dataset -- 3.1 Overview of the Proposed Dataset -- 3.2 How to Create the Dataset -- 3.3 Evaluation Metric on the Proposed Dataset -- 4 Experiments -- 4.1 Learning and Obtaining Word Vectors -- 4.2 Evaluation with Proposed Dataset -- 4.3 Comparison with SimLex-999 -- 5 Conclusion -- References -- Adaptive Database's Performance Tuning Based on Reinforcement Learning -- Abstract -- 1 Introduction -- 2 Related Works -- 3 Adaptive DB Performance Tuning (ADPT) -- 3.1 Process of ADPT -- 3.2 Database's Tools -- 3.3 Subroutines for the RL Agent -- 3.4 RL for DB Tuning: Q Learning -- 4 Empirical Analysis -- 4.1 ADPT Performance and Results -- 4.2 ADPT's Comparative Performance on OLTP, DSS and Hybrid DBs -- 4.3 Discussion -- 5 Conclusion -- References -- Prior-Knowledge-Embedded LDA with Word2vec - for Detecting Specific Topics in Documents -- 1 Introduction -- 2 Related Work -- 3 Implementing Prior Knowledge in LDA -- 3.1 Issue of Topic Assignment to Each Word by LDA. , 3.2 Prior Knowledge to Overcome Inconsistent Alignments of Topics -- 3.3 Contextual LDA to Handle Prior Knowledge -- 3.4 Examining Topic Alignments in Documents -- 4 Data for Examination -- 5 Evaluation of Contextual LDA -- 5.1 Method of Evaluation -- 5.2 Results of Quantitative Analysis -- 5.3 Results of Qualitative Analysis -- 6 Conclusion -- References -- Comparative Analysis of Intelligent Personal Agent Performance -- 1 Introduction -- 1.1 IPA Workflow -- 1.2 C-MCRDR Conversational Agent -- 1.3 Research Contribution -- 2 Related Work -- 2.1 IPA Performance Evaluation -- 2.2 IPA Error Detection and Correction -- 2.3 Ripple down Rules (RDR) -- 3 Method -- 3.1 WER Reduction by Correction -- 3.2 Environment -- 4 Results -- 4.1 BNC Word Letter Count -- 4.2 BNC Rank -- 4.3 Sentence Word Count -- 4.4 C-MCRDR WER Improvement -- 5 Conclusions -- 5.1 Future Work -- References -- Toxicity Prediction by Multimodal Deep Learning -- 1 Introduction -- 2 Preliminaries -- 2.1 SMILES Strings -- 2.2 IGC50 Dataset -- 2.3 Neural Networks -- 3 Methodologies -- 3.1 Vector Representation -- 3.2 Molecular Images -- 3.3 Numerical Features -- 3.4 Input Output -- 3.5 FCNN -- 3.6 CNN -- 3.7 RNN -- 3.8 EA or MNN -- 3.9 Implementation -- 4 Results -- 4.1 Component Neural Networks -- 4.2 Ensemble Performance -- 4.3 Existing Methods -- 4.4 Analyses and Discussions -- 5 Conclusions -- References -- Context-Aware Influence Diffusion in Online Social Networks -- 1 Introduction -- 2 Related Works -- 3 Context-Aware Influence Diffusion Modelling in Social Networks -- 3.1 Preliminaries -- 3.2 Formal Definition -- 3.3 Individual Adoption Behaviour -- 4 Experiments and Discussion -- 4.1 Dataset and Experiment Settings -- 4.2 Experimental Results -- 5 Conclusion and Future Work -- References -- Network Embedding via Link Strength Adjusted Random Walk -- 1 Introduction -- 2 Related Works. , 3 The Proposed Method -- 3.1 Definitions and Notations -- 3.2 Framework -- 3.3 The Link Strength Adjusted Random Walker -- 3.4 The Embedding Calculation -- 3.5 The Link Strength Updating -- 4 Experiment Results -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Node Classification Performances -- 5 Conclusion -- References -- Study on Influencers of Cryptocurrency Follow-Network on GitHub -- 1 Introduction -- 2 Related Work -- 3 Description of the Dataset -- 4 Features of Cryptocurrency Follow-Network -- 5 RQ 1: How Do We Identify an Influencer of the Cryptocurrency Follow-Network? -- 6 RQ 2: Do Influencers Contribute More Than Other Contributors? -- 7 RQ 3: Can Each Project Gain More Contribution Through the Presence of Influencer? -- 8 Conclusion and Future Work -- References -- A Cross-Domain Theory of Mental Models -- 1 Introduction -- 1.1 Conditionals -- 1.2 Syllogisms -- 1.3 Mental Model Theory -- 2 Cross-Domain Reasoning -- 3 Results -- 4 Discussion and Conclusion -- References -- Author Index.

Note: Intro -- Preface -- Organization -- Contents -- Agents and Multiagent Systems -- Lifted Backward Search for General Game Playing -- 1 Introduction -- 2 Related Work -- 3 A Player Based on Backward Search -- 4 Formal Definitions -- 4.1 State Transition Model -- 4.2 Syntax -- 4.3 Semantics -- 5 Backward Search -- 5.1 The C-Operator -- 5.2 The N-Operator -- 5.3 Action Normal Form -- 6 Conclusions -- References -- Corrupt Strategic Argumentation: The Ideal and the Naive -- 1 Introduction -- 2 Background -- 2.1 Abstract Argumentation -- 2.2 Computational Complexity -- 3 Strategic Argumentation -- 4 Corruption in Strategic Argumentation -- 5 Strategic Argumentation Under the Ideal Semantics -- 6 Argumentation Under the Naive Semantics -- 7 Strategic Argumentation Under the Stage Semantics -- 8 Conclusion -- References -- Adaptive Multiagent Reinforcement Learning with Non-positive Regret -- 1 Introduction -- 2 Background -- 2.1 Game Model -- 2.2 Equilibrium States -- 2.3 Regret-Based Reinforcement Learning -- 3 Algorithm -- 3.1 Reinforcement Learning with Non-positive Regret -- 3.2 Discussion -- 3.3 Convergence Analysis -- 4 Evaluation -- 5 Conclusion -- References -- Composability in Cognitive Hierarchies -- 1 Introduction -- 2 Related Work -- 3 Formal Architecture -- 3.1 Nodes -- 3.2 Cognitive Hierarchy -- 3.3 Active Cognitive Hierarchy -- 3.4 Cognitive Process Model -- 4 Behaviour Equivalence -- 5 Node Composition -- 5.1 Parallel and Sequential Composition Operators -- 5.2 Properties -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Enable Efficient Resource Deployment in Multiple Concurrent Emergency Events Through a Decentralised MAS -- 1 Introduction -- 2 Definitions and Problem Description -- 2.1 Definitions of Domain Knowledge -- 2.2 Problem Description -- 3 Theoretical Foundation of the Optimal Resource Coordination. , 4 Agent-Based Resource Allocation -- 4.1 Definitions of Agents -- 4.2 A MAS-based Resource Allocation System -- 5 Experiments -- 5.1 Experimental Setting -- 6 Conclusion -- References -- AI Applications and Innovations -- Forecasting Monthly Rainfall in the Western Australian Wheat-Belt up to 18-Months in Advance Using Artificial Neural Networks -- Abstract -- 1 Introduction -- 2 Data and Method -- 3 Results -- 4 Discussion and Conclusions -- Acknowledgements -- References -- Forecasting Monthly Rainfall in the Bowen Basin of Queensland, Australia, Using Neural Networks with Niño Indices -- Abstract -- 1 Introduction -- 2 Materials and Method -- 3 Results and Discussion -- 3.1 Forecasting ENSO with an ANN -- 3.2 The Spring Predictability Barrier -- 3.3 Forecasting Rainfall for Nebo, Queensland -- 4 Conclusions -- Acknowledgements -- References -- A Cluster Analysis of Stock Market Data Using Hierarchical SOMs -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 4 Validation of the Model -- 5 Experimental Results -- 5.1 Discussion -- 6 Conclusions -- References -- A Generative Deep Learning for Generating Korean Abbreviations -- 1 Introduction -- 2 Related Works -- 3 The Standard Sequence-to-Sequence Model -- 4 Sequence-to-Sequence Learning for Generating Abbreviations -- 5 Experiment -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 6 Conclusion and Future Work -- References -- Medical Prognosis Generation from General Blood Test Results Using Knowledge-Based and Machine-Learning-Based Approaches -- 1 Introduction -- 2 Background -- 2.1 Clinical Decision Support System (CDSS) -- 2.2 Knowledge-Based Approach -- 2.3 ML Approach -- 2.4 Multi-label Classification -- 3 Methods -- 3.1 Knowledge-Based Approach -- 3.2 ML-Based Approach -- 4 Experiments -- 4.1 General Blood Test Data. , 4.2 Experiments for the Knowledge-Based Approach -- 4.3 Experiments for the ML-Based Approach -- 5 Results and Analysis -- 5.1 Results for the Knowledge-Based Approach -- 5.2 Results for the ML-Based Approach -- 6 Conclusion -- References -- Deep Learning for Classification of Malware System Call Sequences -- 1 Introduction -- 2 Methodology -- 2.1 System Description -- 2.2 Dataset -- 2.3 Signature Clustering -- 2.4 Feature Preprocessing -- 2.5 Deep Neural Network -- 3 Evaluation -- 4 Discussion -- 5 Related Work -- 5.1 Machine Learning Methods for Malware Detection -- 5.2 Neural Networks for Malware Detection and Classification -- 6 Conclusion -- References -- Similarity Matching of Computer Science Unit Outlines in Higher Education -- 1 Introduction -- 2 Proposed System Architecture -- 3 Extracting Relevant Keywords -- 3.1 Evaluation -- 4 Assessing Similarity of Keywords from Unit Outlines -- 4.1 Determining Similarity Between Units -- 4.2 Evaluation -- 5 Conclusions and Future Directions -- References -- Parallel Late Acceptance Hill-Climbing Algorithm for the Google Machine Reassignment Problem -- 1 Introduction -- 2 Problem Description -- 3 Methodology -- 3.1 Initialising the Algorithm Parameters -- 3.2 Solution Initialisation and Evaluation -- 3.3 LAHC Procedure -- 3.4 Mutation Operators -- 4 Experiment Settings -- 4.1 GMRP Instances -- 4.2 Parameters Settings -- 5 Results and Comparisons -- 6 Conclusions -- References -- Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers -- 1 Introduction -- 2 The OHNBC Algorithm -- 3 Experimental Design and Results -- 3.1 Parameter Optimization -- 3.2 Results -- 4 Conclusion -- References -- Visual Odometry in Dynamic Environments with Geometric Multi-layer Optimisation -- 1 Introduction -- 2 Related Work -- 3 Geometric Multi-layer Optimisation -- 4 Three-State Kalman Filter. , 5 Evaluation -- 6 Summary -- References -- High Resolution SOM Approach to Improving Anomaly Detection in Intrusion Detection Systems -- 1 Introduction and Problem Description -- 2 KDD Cup 1999 Dataset -- 3 Design of Experiments -- 4 Results of Experiments -- 5 Conclusion -- References -- Big Data -- CPF: Concept Profiling Framework for Recurring Drifts in Data Streams -- 1 Introduction -- 2 Related Work -- 3 Concept Profiling Framework (CPF) -- 3.1 Model Similarity -- 3.2 Reuse and Representation -- 3.3 Fading -- 3.4 Model Management in Practice -- 4 Experimental Design and Results -- 4.1 Datasets -- 4.2 CPF Fading Mechanism -- 4.3 CPF Similarity Margin -- 4.4 Comparison with RCD on Synthetic Datasets -- 4.5 Comparison with RCD on Real-World Datasets -- 5 Conclusion and Further Work -- References -- Meta-mining Evaluation Framework: A Large Scale Proof of Concept on Meta-learning -- 1 Introduction -- 2 Example of Meta-mining Experiment -- 3 Dimensions of Study -- 4 Experiment Setup -- 5 Results Interpretation -- 6 Conclusion -- References -- Bayesian Grouped Horseshoe Regression with Application to Additive Models -- 1 Introduction -- 1.1 Grouped Variables -- 1.2 Bayesian Regression -- 2 Bayesian Grouped Horseshoe Models -- 2.1 Bayesian Horseshoe Model -- 2.2 Bayesian Grouped Horseshoe Model -- 2.3 Hierarchical Bayesian Grouped Horseshoe Model -- 3 Additive Models -- 4 Simulation Studies -- 4.1 Simulation Procedures -- 4.2 Test Functions -- 4.3 Simulation Results -- 5 Real Data -- References -- Constraint Satisfaction, Search and Optimisation -- Improving and Extending the HV4D Algorithm for Calculating Hypervolume Exactly -- 1 Introduction -- 2 Background Material -- 2.1 Multi-objective Optimisation -- 2.2 Hypervolume -- 2.3 Previous Algorithms for Calculating Exact Hypervolumes -- 3 The HV4D Algorithm -- 4 Extensions to HV4D -- 4.1 HV4DR. , 4.2 HV4DX -- 4.3 HV5DR -- 5 Results -- 6 Conclusions -- References -- Local Search for Maximum Vertex Weight Clique on Large Sparse Graphs with Efficient Data Structures -- 1 Introduction -- 2 Preliminaries -- 2.1 Basic Notations -- 2.2 The Large Crafted Benchmark -- 2.3 Multi-neighborhood Greedy Search -- 2.4 The Strong Configuration Checking Strategy -- 2.5 Best from Multiple Selections (BMS) -- 3 Local Move Yielded by Greedy and Random Selections -- 4 The LMY-GRS Algorithm -- 5 Data Structures -- 5.1 Connect Clique Degrees and Clique Neighbors -- 5.2 A Hash Table for Determining Neighbor Relations -- 6 Experimental Evaluation -- 6.1 Experiment Setup -- 6.2 Main Results -- 7 Conclusions and Future Work -- References -- Cascade Bayesian Optimization -- 1 Introduction -- 2 Background -- 2.1 Gaussian Process -- 2.2 Acquisition Functions for Bayesian Optimization -- 3 The Proposed Solution -- 4 Experiments -- 4.1 Baseline Method and Evaluation Measure -- 4.2 Experiments with Synthetic Data -- 4.3 Experiments with Real Data -- 4.4 Cost-Efficient Optimization -- 5 Conclusion -- References -- Assignment Precipitation in Fail First Search -- 1 Introduction -- 2 Constraint Preliminaries -- 3 Our Precipitation -- 3.1 Condensing Operation -- 3.2 Assignment Relocation -- 4 Related Work -- 5 Experimental Results -- References -- Knowledge Representation and Reasoning -- Update Policies -- 1 Introduction -- 2 Problem Definition -- 3 Update Policy Language -- 3.1 Overview of Datalog -- 3.2 Constraints in Datalog -- 3.3 Datalogu: Extending Datalog with Update Operators -- 3.4 Expressiveness of Datalogu -- 4 Verification of Update Policies -- 5 Conclusion and Outlook -- References -- Utilization of DBpedia Mapping in Cross Lingual Wikipedia Infobox Completion -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Cross Language Infobox Completion. , 3.1 Mapping Tables.

Note: Intro -- Preface -- Organization -- Contents -- Accelerating the Backpropagation Algorithm by Using NMF-Based Method on Deep Neural Networks -- 1 Introduction -- 2 Training of DNN -- 2.1 Backpropagation -- 2.2 NMF-Based Algorithm -- 3 Proposed Method -- 4 Experimental Results -- 4.1 Preliminary Experiments on the BP and the NMF-Based Algorithm -- 4.2 Performance Evaluation -- 5 Conclusion -- References -- Collaborative Data Analysis: Non-model Sharing-Type Machine Learning for Distributed Data -- 1 Introduction -- 2 Distributed Data -- 3 Collaborative Data Analysis -- 3.1 Basic Concept -- 3.2 Derivation of the Proposed Method -- 3.3 Practical Operation Strategy Regarding Privacy and Confidentiality Concerns -- 4 Numerical Experiments -- 4.1 Experiment I: For Artificial Data -- 4.2 Experiment II: vs. Number of Parties -- 4.3 Experiment III: For Real-World Data -- 4.4 Remarks on Numerical Results -- 5 Conclusions -- References -- ERA: Extracting Planning Macro-Operators from Adjacent and Non-adjacent Sequences -- 1 Introduction -- 2 Background Theory -- 3 ERA -- 3.1 Overview -- 3.2 ERA Algorithm -- 3.3 Mining Procedure -- 3.4 Complexity Analysis. -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Results of the ERA Algorithm -- 5 Discussion -- 6 Conclusion -- References -- Deep Neural Network Incorporating CNN and MF for Item-Based Fashion Recommendation -- 1 Introduction -- 2 Related Works -- 2.1 Outfit Recommendation -- 2.2 Item-Based Recommendation -- 3 Methodology -- 3.1 Offline Training Phase -- 3.2 Item Recommendation Phase -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Method -- 4.3 Comparison Methods -- 4.4 Performance -- 4.5 Online Evaluation -- 5 Conclusion -- References -- C-LIME: A Consistency-Oriented LIME for Time-Series Health-Risk Predictions -- 1 Introduction -- 2 Related Works. , 2.1 Risk Prediction Based on Electronic Health Records (EHRs) -- 2.2 Explainable Artificial Intelligence (XAI) -- 2.3 Summary -- 3 Proposed Method -- 3.1 Health-Risk Prediction Algorithm -- 3.2 Interpretation of Health-Risk Prediction by LIME -- 3.3 C-LIME: Consistently Explainable LIME -- 4 Evaluation -- 4.1 Accuracy of Health-Risk Prediction Model -- 4.2 Interpretation of Health Risk Prediction Model Using C-LIME -- 5 Health-Risk Prediction and Lifestyle Recommendation Service -- 6 Conclusions -- References -- Discriminant Knowledge Extraction from Electrocardiograms for Automated Diagnosis of Myocardial Infarction -- 1 Introduction -- 2 Our Approach -- 2.1 Overview -- 2.2 Modelling Spectral and Longitudinal Characteristics -- 3 Experiments -- 3.1 Datasets -- 3.2 Setup -- 3.3 Results and Discussion -- 4 Conclusions -- References -- Stabilizing the Predictive Performance for Ear Emergence in Rice Crops Across Cropping Regions -- 1 Introduction -- 2 Related Studies -- 3 Data -- 3.1 Cropping Records -- 3.2 Micro Climate Data -- 3.3 Partitioned Regions -- 4 Proposal - Engineering Variables -- 4.1 Clustering Regional Time Series Patterns -- 4.2 Deriving the Time Series Statistics -- 5 Predicting Procedure -- 5.1 Datasets -- 5.2 Executing Predictions -- 5.3 Evaluating Predictive Performance -- 6 Evaluation -- 7 Discussion -- 8 Conclusion -- References -- Description Framework for Stakeholder-Centric Value Chain of Data to Understand Data Exchange Ecosystem -- 1 Introduction -- 2 Data Exchange Ecosystem and Relevant Studies -- 3 Stakeholder-Centric Value Chain of Data -- 4 Experimental Details -- 5 Results and Discussion -- 5.1 Structural Characteristics of the Data Exchange Ecosystem -- 5.2 Knowledge Extraction from SVC -- 5.3 Limitations and Future Work -- 6 Conclusion -- References -- Attributed Heterogeneous Network Embedding for Link Prediction. , 1 Introduction -- 2 Related Work -- 3 The Proposed Model -- 3.1 Preliminaries -- 3.2 AHNE -- 4 Experimental Evaluation -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Experimental Results -- 5 Conclusion -- References -- Automatic Generation and Classification of Malicious FQDN -- 1 Introduction -- 2 Related Work -- 3 Criticism of String-Based Approach -- 4 FakePopular and Its Implication -- 5 Conclusion -- References -- Analyzing Temporal Change in LoRa Communication Quality Using Massive Measurement Data -- 1 Introduction -- 2 Related Works -- 3 Bus Location Management System and Its Log Data -- 4 Proposed Method -- 5 Analysis -- 5.1 Effects of Rainfall on RSSI -- 5.2 Effects of Fallen Snow on RSSI -- 6 Conclusion -- References -- Challenge Closed-Book Science Exam: A Meta-Learning Based Question Answering System -- 1 Introduction -- 2 Related Work -- 2.1 Standardized Science Exams -- 2.2 Meta-Learning -- 3 Meta-Classifier System -- 3.1 Few-Shot Question Classification -- 3.2 Model Agnostic Meta-Learning Method -- 4 Reasoning System -- 5 Experiments -- 5.1 Few-Shot Question Classification -- 5.2 Model Visualization -- 5.3 Question Answering with Few-Shot QC Information -- 5.4 Case Study -- 6 Conclusion -- References -- Identification of B2B Brand Components and Their Performance's Relevance Using a Business Card Exchange Network -- 1 Introduction -- 2 Related Work -- 3 Description of Dataset -- 3.1 Eight Company Score -- 3.2 Definition of Corporate Brand Score -- 3.3 Definition of Corporate Performance -- 4 RQ1: Is B2B Company Brand Related to Corporate Performance? -- 4.1 Discussion -- 5 RQ2: What Are the Components of a B2B Company's Brand Impression? -- 5.1 Supervised Topic Models -- 5.2 Relationship Between Each Latent Topic and Corporate Performance -- 5.3 Discussion -- 6 Conclusion -- References. , Semi-automatic Construction of Sight Words Dictionary for Filipino Text Readability -- 1 Introduction -- 2 Related Works -- 3 Extracting Seed Words from Storybooks -- 4 Expanding the Dictionary -- 4.1 Word Embeddings -- 4.2 Dictionary Entries -- 5 Discussion -- 5.1 Readability Assessment -- 5.2 Oral Reading Fluency -- 6 Conclusion and Future Work -- References -- Automated Concern Exploration in Pandemic Situations - COVID-19 as a Use Case -- 1 Introduction -- 2 Related Works -- 3 Automated Public Concern Detection -- 3.1 Data Pre-processing and Information Extraction -- 3.2 Deep Learning Models -- 3.3 Concern Extraction and Clustering -- 3.4 Concern Knowledge Graph -- 4 Experiments -- 4.1 Dataset -- 4.2 Results Analysis -- 4.3 Result Visualisation -- 5 Conclusion and Future Work -- References -- Author Index.

Note: Title -- Preface -- Contents -- Part I Provenance in eScience: Representation and Use -- Provenance Model for Randomized Controlled Trials -- Introduction -- Process Flow of Clinical Trials -- Trial Planning and Development -- Conduct of the Clinical Trial Process (Trial Management) -- Trial Ending -- Trial Metadata Analysis -- ICH GCP and Other Models -- Provenance -- Provenance in Healthcare -- Open Provenance Model -- Profiles -- OPM RCT Profile Proposal -- RCT Controlled Vocabulary -- Profile Expansion Rules -- Examples -- Storage and Analysis of Provenance Data -- Summary -- References -- Evaluating Workflow Trust Using Hidden Markov Modeling and Provenance Data -- Introduction -- Motivation and Requirements -- Architecture -- Hidden Markov Modeling for the Evaluation ofWorkflow Trust -- Methodology -- Trust Model Assessment -- Cases with Dynamic or Parallel Sections -- Implementation -- Verification of the Model -- Case Study -- Investigation of the Stationary Assumption -- Conclusion and Future Work -- References -- Unmanaged Workflows: Their Provenance and Use -- Introduction -- Provenance Creation -- Overview -- Application in the Karma Tool -- Provenance Representation -- Representation in Karma -- Provenance Use -- Using Provenance to Aid Workflow Construction -- Using Data Provenance Traces to Reconstruct Process Traces -- Using Provenance for Analysis of Workflow Traces -- RelatedWork -- Current and Future Challenges -- References -- Part II Data Provenance and Data Management Systems -- Sketching Distributed Data Provenance -- Introduction -- RelatedWork -- Tracking System-Level Provenance with SPADE -- Intra-host Dependencies -- Inter-host Dependencies -- Querying Provenance -- Provenance Sketches -- Matrix Filter -- SPADE's Use of Matrix Filters -- Experimental Results -- Reduction in Network Latency -- Sketch Robustness. , Conclusion -- References -- A Mobile Cloud with Trusted Data Provenance Services for Bioinformatics Research -- Introduction -- The Application Scenario -- A Mobile Cloud System for Bioinformatics Research -- Overall Architecture of the Mobile Cloud -- Workflow Design through Abstract Description Script -- Accountability for Trusted Data Provenance -- Accountability for Trustworthiness -- Logging Provenance Data at Trusted Provenance Unit -- Architectural Design of Trusted Provenance Unit -- Prototype Implementation -- RelatedWork -- Conclusions and Future Work -- References -- Data Provenance and Management in Radio Astronomy: A Stream Computing Approach -- Introduction -- IBM InfoSphere Streams and the Stream-Computing Paradigm -- InfoSphere Streams Terminology and Concepts -- Data Streaming Applications with SPADE -- Deploying SPADE Applications and Performance Optimization -- Utilizing InfoSphere Streams to Address Large Antennae Array Software Architecture -- Data Provenance and Management Capabilities -- Some Applications of Streams in Radio Astronomy -- Implementing a Stream-Centric Autocorrelation DataPipeline & -- Utilizing Hardware Accelerators -- Autocorrelation and the Power Spectral Density in Radio Astronomy -- Implementing a PSD Pipeline as a Stream Based Application -- Using Accelerators (Heterogeneous Computing) -- Testing the SPADE PSD Application -- Performance and Scalability -- Conclusion -- Appendix -- References -- Using Provenance to Support Good Laboratory Practice in Grid Environments -- Introduction -- A Sample Use Case -- Data Management with the DataFinder -- Overview -- Provenance Management -- OPM - Open Provenance Model -- Provenance Storage with prOOst -- Distributed, Scientific Data Management -- Integration of Existing Storage Servers -- Designing an Alternative Storage Concept - MataNui -- Results. , Developing a Provenance Model for Good Laboratory Practice -- Adjustments for Good Laboratory Practice in the DataFinder -- Integration Evaluation of an Electronic Laboratory Notebook -- Outlook: Improving DataFinder-Based Laboratory Notebook -- Conclusion -- References -- Author Index.

Note: Intro -- Preface -- Contents -- Contributors -- Part I Agent-Based Complex Automated Negotiations -- BiTrust: A Comprehensive Trust Management Model for Multi-agent Systems -- 1 Introduction -- 2 Related Work -- 3 BiTrust: Bijection Trust Management Model -- 3.1 Definitions -- 3.2 The BiTrust Protocol -- 3.3 Consumer Behaviour Reasoning -- 3.4 Service Provider Selection -- 4 Experimental Results and Discussion -- 5 Conclusion and Future Work -- References -- Using Reference Points for Competitive Negotiations in Service Composition -- 1 Introduction -- 2 Composing QoS-Based Services -- 2.1 Service Selection -- 3 Negotiation Requirements for Service Composition -- 3.1 One-to-Many -- 3.2 Incomplete Information -- 3.3 Multi-issue -- 3.4 Coordinated Interaction -- 4 The Negotiation Formalization -- 4.1 The Agents Bidding Strategy -- 4.2 Weighted Reference Points -- 5 Simulation Results -- 6 Related Work -- 7 Conclusions -- References -- A Cooperative Framework for Mediated Group Decision Making -- 1 Introduction -- 2 Problem Description -- 2.1 Negotiation Domain and Agents' Preferences -- 2.2 Negotiation Protocol -- 2.3 Aggregation Operator for Adapted GPS (GPSao) -- 2.4 Contract Selection -- 3 Experimental Evaluation -- 3.1 Proof of Concept Scenario -- 3.2 Highly Non-linear Utility Functions -- 4 Discussion -- 5 Conclusions -- References -- A Dependency-Based Mediation Mechanism for Complex Negotiations -- 1 Introduction -- 2 Negotiation Environments -- 2.1 Multi-issue Automated Negotiations -- 2.2 Nonlinear Utility Models -- 2.3 Dependency-Based Hypergraphical Utility Model -- 3 A Dependency-Based Mediation Mechanism -- 3.1 Negotiation Protocol -- 3.2 Dependency-Based Mediation Strategy -- 4 Experimental Results -- 4.1 Settings -- 4.2 Discussion -- 5 Related Work -- 6 Conclusions -- References. , Using Graph Properties and Clustering Techniques to Select Division Mechanisms for Scalable Negotiations -- 1 Introduction -- 2 Complex Self-interested Networks (CSIN) -- 3 Proof-of-Concept Domain: Chessboard Evacuation -- 3.1 Formalisation of the Problem -- 3.2 Modeling Agent Self-interests -- 3.3 Categories of Scenarios -- 4 Graph Metrics for Scenario Characterization -- 5 Using Graph Metrics for Mechanism Selection -- 5.1 Distributed, Mediated Division Approaches -- 5.2 Negotiation Between Agents -- 5.3 Experimental Setting -- 5.4 Experimental Results -- 6 Discussion and Conclusions -- References -- Compromising Strategy Considering Interdependencies of Issues for Multi-issue Closed Nonlinear Negotiations -- 1 Introduction -- 2 Related Works -- 3 Negotiation Environments -- 4 SPEA2 for Finding Pareto Frontier -- 5 Strategy Considering Interdependency Between Issues -- 5.1 Estimating Opponent's Utility Considering Interdependency Between Issues -- 5.2 Automated Negotiating Agent Considering Issue Interdependency -- 6 Experimental Results -- 6.1 Finding the Pareto Optimal Bids -- 6.2 Tournament Results with ANAC-2014 Finalists -- 7 Conclusions -- References -- A Negotiation-Based Model for Policy Generation -- 1 Introduction -- 2 A Negotiation-Based Model -- 2.1 A Policy Structure -- 2.2 Evaluation Methods of an Offer -- 2.3 Negotiation Protocol -- 2.4 Agreement Generation -- 3 Fuzzy Reasoning -- 3.1 Fuzzy Linguistic Terms of Fuzzy Variables -- 3.2 Fuzzy Rules -- 3.3 Fuzzy Inference Method -- 4 A Priority Operator -- 5 Illustration -- 6 Experiment -- 6.1 Experimental Setting -- 6.2 Results and Analysis -- 7 Related Work -- 8 Conclusions and Future Work -- References -- Fixed-Price Tariff Generation Using Reinforcement Learning -- 1 Introduction -- 2 Power TAC and Tariff Markets -- 3 COLD Energy Tariff-Expert -- 3.1 Market Model. , 3.2 MDP Description -- 3.3 States -- 3.4 Actions -- 3.5 State/Action Flow Example -- 4 Experimental Results -- 4.1 General Setup -- 4.2 Experiments Description -- 4.3 COLD Energy Versus All -- 4.4 COLD Energy Versus ReddyLearning -- 5 Conclusions -- References -- Part II Automated Negotiating Agents Competition -- The Sixth Automated Negotiating Agents Competition (ANAC 2015) -- 1 Introduction -- 2 Setup of ANAC 2015 -- 2.1 Negotiation Model -- 2.2 Running the Tournament -- 3 Competition Domains and Agents -- 3.1 Scenario Descriptions -- 3.2 Agent Descriptions -- 4 Competition Results -- 4.1 Qualifying Round -- 4.2 Final Round -- 5 Conclusion -- References -- Alternating Offers Protocols for Multilateral Negotiation -- 1 Introduction -- 2 Formal Framework for Multilateral Turn-Taking Protocols -- 2.1 Basic Notation -- 3 Stacked Alternating Offers Protocol (SAOP) -- 3.1 Example -- 4 Alternating Multiple Offers Protocol (AMOP) -- 4.1 Illustration -- 5 Experimental Evaluation -- 6 Discussion -- 7 Conclusion -- References -- Atlas3: A Negotiating Agent Based on Expecting Lower Limit of Concession Function -- 1 Introduction -- 2 Searching Methods -- 3 Expecting Lower Limit of Concession Function -- 4 Conclusion -- References -- Pars Agent: Hybrid Time-Dependent, Random and Frequency-Based Bidding and Acceptance Strategies in Multilateral Negotiations -- 1 Introduction -- 2 Acceptance Strategy -- 3 Bidding Strategy -- 3.1 Bidding Strategy 1: Pars Agent Moves First -- 3.2 Bidding Strategy 2: The Other Party Moves First -- 4 Results -- 5 Conclusion -- References -- RandomDance: Compromising Strategy Considering Interdependencies of Issues with Randomness -- 1 Estimating Utility Functions by Counting Values -- 2 Weighted Sum of the Estimated Utility -- 3 Strategy of RandomDance -- 4 Conclusion -- Reference. , Agent Buyog: A Negotiation Strategy for Tri-Party Multi Issue Negotiation -- 1 Introduction -- 2 Agent Buyog Strategy -- 2.1 Strategy Overview -- 2.2 Learning Function -- 2.3 Consensus Factor -- 2.4 Concession Curve -- 2.5 Bidding -- 2.6 Acceptance -- 2.7 Miscellaneous -- 3 Conclusion and Future Works -- References -- Phoenix: A Threshold Function Based Negotiation Strategy Using Gaussian Process Regression and Distance-Based Pareto Frontier Approximation -- 1 Introduction -- 2 Related Work -- 3 The Phoenix Strategy -- 3.1 Threshold Function Construction (TFC) Component -- 3.2 Decision Making (DM) Component -- 4 Performance Analysis -- 4.1 Parameters Setting -- 4.2 Results and Analysis -- 5 Conclusions and Future Work -- References -- Pokerface: The Pokerface Strategy for Multiparty Negotiation -- 1 Introduction -- 2 Strategy -- 2.1 First Stage: Random Walk -- 2.2 Second Stage: Conceding -- 2.3 Opponent Model -- 2.4 Final Round -- 3 Experiments and Evaluation -- 4 Conclusions and Future Work -- References -- Negotiating with Unknown Opponents Toward Multi-lateral Agreement in Real-Time Domains -- 1 Introduction -- 2 Multilateral Negotiation Model -- 3 Negotiation Approach -- 3.1 Deciding Aspiration Level -- 3.2 Generating Offers -- 3.3 Responding Mechanism -- 4 Conclusion -- References -- Jonny Black: A Mediating Approach to Multilateral Negotiations -- 1 Introduction -- 2 Strategy -- 2.1 Parameters -- 2.2 Initialization -- 2.3 Opponent Modeling -- 2.4 Accepting Strategy -- 2.5 Bidding Strategy -- 3 Conclusion and Future Work -- References -- Agent X -- 1 Introduction -- 2 Strategy of Agent X -- 2.1 Agreement Behaviour -- 2.2 Conceder Behaviour -- 3 Results of Simulation -- 4 Conclusion -- References -- CUHKAgent2015: An Adaptive Negotiation Strategy in Multilateral Scenario -- 1 Introduction -- 2 Design Issues. , 2.1 Reasons of Not Predicting Opponent's Decision Function -- 2.2 Factors Determining Degree of Concession to Opponent -- 2.3 Guessing Opponents' Preferences - Approaching the Pareto Frontier -- 3 Strategy Description -- 3.1 Overall Flow of Decision -- 3.2 Separated Acceptance and Bidding Thresholds for Different Opponents -- 3.3 Combining the Thresholds and Bid-Proposing -- 3.4 Approaching the Pareto Frontier -- 4 Conclusion -- References -- AgentH -- 1 Introduction -- 2 Implementation of AgentH -- 2.1 Compromising Strategy -- 2.2 Offering New Bids -- 3 Conclusion -- References.