Anmerkung: Intro -- Preface -- Organization -- Contents -- The Power of Signaling and Its Intrinsic Connection to the Price of Anarchy -- 1 Introduction -- 2 Preliminaries -- 2.1 Cost-Minimization/Payoff-Maximization Games -- 2.2 Signaling Schemes and Equilibrium Concepts -- 3 The Power of Signaling (PoS) -- 4 PoS in Cost-Minimization Games -- 4.1 Proof of the PoS Upper Bounds -- 4.2 Tightness of the Upper-Bound for PoS -- 5 PoS in Payoff-Maximization Games -- 5.1 Tightness of PoS(Pri:Pub) at the Robber's Game -- 6 Discussions and Future Work -- A Omitted Proofs for Cost-Minimization PoS Bounds -- A.1 Tightness of the Upper-Bound for PoS(Pri:Pub) -- A.2 Tightness of the Upper-Bound for PoS(exP:Pri) -- B Omitted Proofs for Payoff-Maximization PoS Bounds -- B.1 Tightness of the Lower-Bound for PoS(Pub:FI) -- B.2 Tightness of the Lower-Bound for PoS(exP:Pri) -- C PoS w.r.t the No Information (NI) Benchmark -- D Non-tightness of PoS in ``Reverse'' Routing -- References -- Uncertainty-Aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning -- 1 Introduction -- 2 Background -- 2.1 Reinforcement Learning -- 2.2 Approximate Rank and Matrix Reconstruction -- 3 Low-Rank Q-Matrix in DRL -- 3.1 Empirical Study of Low-Rank Q-Matrix in MuJoCo -- 3.2 Q-Matrix Reconstruction for DRL -- 4 DRL with Uncertainty-Aware Q-Matrix Reconstruction -- 4.1 Connection Between Rank and Uncertainty -- 4.2 Uncertainty-Aware Q-Matrix Reconstruction for DRL -- 5 Experiment -- 5.1 Experiment Setup -- 5.2 Results and Analysis -- 6 Conclusion -- A More Background on Matrix Estimation -- B Additional Experimental Details -- C Complete Learning Curves of Table2 -- References -- SEIHAI: A Sample-Efficient Hierarchical AI for the MineRL Competition -- 1 Introduction -- 2 Background -- 2.1 The MineRL Competition -- 2.2 The ObtainDiamond Task -- 2.3 The ObtainDiamond MDP. , 3 Method -- 3.1 The Overall Framework -- 3.2 Action Discretization -- 3.3 The ChopTree Agent -- 3.4 The CraftWoodenPickaxe Agent -- 3.5 The DigStone Agent -- 3.6 The CraftStonePickaxe Agent -- 3.7 The RandomSearch Agent -- 3.8 The Scheduler -- 4 Experiments -- 4.1 Overall Evaluation -- 4.2 Agent-Level Evaluation -- 5 Related Work -- 6 Conclusion -- References -- BGC: Multi-agent Group Belief with Graph Clustering -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Graph Attention Network -- 4 Method -- 4.1 Adjacent Matrix via kNN -- 4.2 Belief in Graph Clustering -- 4.3 Split Loss -- 4.4 Decentralization Execution -- 5 Experiment -- 5.1 Starcraft II -- 5.2 Representation -- 5.3 Ablation -- 5.4 Distributed Execution -- 6 Conclusion -- References -- Incomplete Distributed Constraint Optimization Problems: Model, Algorithms, and Heuristics -- 1 Introduction -- 2 Background -- 3 Incomplete DCOPs -- 4 Solving I-DCOPs -- 4.1 SyncBB -- 4.2 ALS-MGM -- 5 SyncBB Cost-Estimate Heuristic -- 6 ALS-MGM Cost-Estimate Heuristic -- 7 Theoretical Results -- 8 Related Work -- 9 Empirical Evaluations -- 10 Conclusions -- References -- Securities Based Decision Markets -- 1 Introduction -- 2 Related Work and Notation -- 2.1 Scoring Rules -- 2.2 Sequentially Shared Scoring Rules -- 2.3 Securities Based Prediction Markets -- 2.4 Decision Markets -- 3 Strictly Proper Securities Based Decision Markets -- 3.1 Design -- 3.2 Distribution of Realised Payoffs -- 4 Worst-Case Losses for Participants and Market Creator -- 4.1 Worst-Case Loss for Participants -- 4.2 Worst-Case Loss for Market Creator -- 4.3 Re-allocation of Worst-Case Losses -- 5 Conclusion and Discussion -- References -- MARL for Traffic Signal Control in Scenarios with Different Intersection Importance -- 1 Introduction -- 2 Basic Notation -- 2.1 Adaptive Traffic Signal Control -- 2.2 Network Markov Game. , 2.3 Deep Q-Learning and HDQN -- 3 Leader Follower Markov Game -- 4 Breadth First Sort Hysteretic DQN -- 5 Experiment -- 5.1 Scenarios Setting -- 5.2 Training Setting -- 5.3 Performance Comparison -- 6 Conclusion -- References -- Safe Distributional Reinforcement Learning -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Problem Formulation -- 5 Proposed Method -- 5.1 General Principle -- 5.2 Techniques for Efficient Implementation -- 6 Experimental Results -- 6.1 Safety Gym -- 6.2 Stock Investment -- 7 Conclusion -- A Performance Guarantee Bound -- B More Results -- B.1 Random CMDP -- B.2 Safety Gym -- C More Details on Experiments -- C.1 Random CMDP -- C.2 Stock Transaction -- C.3 Mujoco Simulator -- References -- The Positive Effect of User Faults over Agent Perception in Collaborative Settings and Its Use in Agent Design -- 1 Introduction -- 2 Related Work -- 3 The Model -- 4 Research Hypotheses -- 5 Experimental Framework -- 6 Experimental Design -- 6.1 Experimental Treatments -- 6.2 Measures -- 7 Results and Analysis -- 7.1 The Effect of User's Own Faults over Her Satisfaction with the Collaborative Agent -- 7.2 Incorporating User Faults in Agent Design - Proof of Concept -- 7.3 Participants' Qualitative (Textual) Responses -- 8 Discussion, Conclusions and Future Work -- A Experimental Framework Interface -- B Experimental Treatments Comparison -- C State Machines -- D Measures -- E Complementary Graphs -- E.1 Competence -- E.2 Recommendation -- F Participants' Qualitative (Textual) Responses -- References -- Behavioral Stable Marriage Problems -- 1 Introduction -- 2 Multialternative Decision Field Theory (MDFT) -- 3 Stable Marriage Problems (SMPs) -- 4 Behavioral Stable Marriage Problems (BSMPs) -- 5 Complexity Results -- 6 Algorithms for BSMPs -- 7 Experimental Results -- 8 Future Work -- A Proofs of Theorems -- B Algorithms. , C Convergence Analysis for B-LS -- References -- FUN-Agent: A HUMAINE Competitor -- 1 Introduction -- 2 Related Work -- 3 2020 HUMAINE Competition -- 3.1 Language Processing -- 3.2 Formal Specification of the Repeated Task Negotiation Problem -- 4 Strategy -- 4.1 Conversational Demeanor -- 4.2 Bundling -- 4.3 State Tracking -- 4.4 Conceding -- 4.5 Obfuscation as a Competitive Strategy -- 4.6 Initial Offer Generation -- 4.7 Counteroffer Generation -- 4.8 Guarding Against Competitor Sniping -- 5 Experimentation and Design -- 5.1 Competitor Agents -- 5.2 Human Negotiators -- 5.3 Negotiation Rounds -- 6 Results and Discussion -- 7 Conclusion -- References -- Signal Instructed Coordination in Cooperative Multi-agent Reinforcement Learning -- 1 Introduction -- 2 Methods -- 2.1 Preliminaries -- 2.2 Joint Policy Space with Coordination Signal -- 2.3 Signal Instructed Coordination -- 2.4 Implementation Details -- 3 Experiments -- 3.1 Rock-Paper-Scissors-Well (RPSW) -- 3.2 Particle Worlds -- 4 Related Works -- 5 Conclusions -- A Algorithm -- B Proof of Proposition 1 -- C Proof of Proposition 2 -- D Derivation of the Lower Bound -- E Experiment Details -- E.1 Matrix Game Experiment -- E.2 Particle World Experiment -- F Visualization for Joint Policy of Multi-step Matrix Game -- G Parameter Sensitivity -- References -- A Description of the Jadescript Type System -- 1 Introduction and Motivation -- 2 The Jadescript Type System -- 2.1 Basic Types -- 2.2 Collection Types -- 2.3 Concept, Action, Predicate, and Proposition Types -- 2.4 Ontology Types -- 2.5 Agent Types -- 2.6 Behaviour Types -- 2.7 Message Types -- 3 Related Work -- 4 Conclusions -- References -- Combining M-MCTS and Deep Reinforcement Learning for General Game Playing -- 1 Introduction -- 2 Background -- 2.1 General Game Playing -- 2.2 Memory-Augmented Monte Carlo Tree Search. , 2.3 Deep Reinforcement Learning -- 3 Method -- 3.1 M-MCTS for GGP -- 3.2 Combining M-MCTS with Deep Reinforcement Learning -- 4 Experimental Evaluation -- 4.1 Evaluation Methodology -- 4.2 Results and Analysis -- 5 Conclusion -- References -- A Two-Step Method for Dynamics of Abstract Argumentation -- 1 Introduction -- 2 Preliminaries -- 3 The Update of Argumentation Frameworks -- 4 Properties -- 5 Related Work -- 6 Conclusion and Further Work -- References -- Author Index.