
Multi-Agent Systems
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The 18 full papers presented in this volume were carefully reviewed and selected from a total of 34 submissions. The papers report on both early and mature research and cover a wide range of topics in the field of multi-agent systems.
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Content
- Intro
- Preface
- Organization
- Contents
- Temporal Epistemic Gossip Problems
- 1 Introduction
- 2 Definitions
- 3 Membership in NP
- 4 A Subproblem of the Temporal Gossip Problem in P
- 5 NP-completeness When Execution Time Is Bounded
- 6 NP-completeness of Gossiping with Negative Goals
- 7 Discussion and Conclusion
- References
- Partial and Full Goal Satisfaction in the MUSA Middleware
- 1 Introduction
- 2 Motivation
- 3 Full and Partial Goal Satisfaction
- 4 The Agent and Artifact Architecture
- 4.1 Implementing the Metric for Partial Satisfaction
- 5 Discussion
- 6 Conclusions
- References
- Generalising the Dining Philosophers Problem: Competitive Dynamic Resource Allocation in Multi-agent Systems
- 1 Introduction and Related Work
- 2 Generalisation of the Dining Philosophers Problem
- 3 Logic for Specification and Verification of GDP Games
- 4 Symbolic Representation of Configurations on GDP Games
- 5 Symbolic Verification of LGDP Formulae in GDP Games
- 6 Conclusions and Outlook to Future Work
- A Appendix: Proof of Theorem1
- References
- Interpreting Information in Smart Environments with Social Patterns
- 1 Introduction
- 2 Background
- 3 The SCAA Framework
- 3.1 Social Information
- 3.2 Architecture
- 4 Case Study: Distributed Collaborative Care System
- 5 Related Work
- 6 Conclusions
- References
- Learning Hedonic Games via Probabilistic Topic Modeling
- 1 Introduction
- 2 Background and Related Work
- 2.1 Hedonic Games
- 2.2 Probabilistic Topic Modeling
- 2.3 Uncertainty
- 3 Game Interpretation as Documents
- 4 Experimental Evaluation
- 4.1 Dataset and Setting Escalation
- 4.2 Significant Agents and Valid Topics
- 4.3 Results
- 5 Conclusion and Future Work
- A Appendix: Detailed Results
- References
- Learning Best Response Strategies for Agents in Ad Exchanges
- 1 Introduction
- 2 Related Work
- 3 Model for the Publisher in an Ad Exchange
- 3.1 HBA Types (Advertiser's Strategies)
- 3.2 HBA Beliefs and Best Responses
- 3.3 HBA Censored
- 3.4 KM Estimator for Stochastic Opponents
- 4 Experimental Results
- 4.1 Agents in the Type Space A
- 4.2 Neural Network Agent
- 5 Discussion and Conclusions
- References
- Towards Online Electric Vehicle Scheduling for Mobility-On-Demand Schemes
- 1 Introduction
- 2 Problem Formulation
- 3 Scheduling Algorithms
- 3.1 Short Mode Algorithm
- 3.2 Long Mode Algorithm
- 4 MOD Software Package
- 4.1 Web Platform
- 4.2 Mobile Application
- 5 Testing and Evaluation
- 6 Conclusions and Future Work
- References
- Two-Sided Markets: Mapping Social Welfare to Gain from Trade
- 1 Introduction
- 2 Preliminaries
- 3 Converting Social Welfare to Gain from Trade
- 4 Experimental Results
- 5 Conclusion and Discussion
- References
- Affective Decision-Making in Ultimatum Game: Responder Case
- 1 Introduction
- 2 Preliminaries
- 2.1 General Notation
- 2.2 Ultimatum Game Rules
- 2.3 Markov Decision Process
- 3 Ultimatum Game as Markov Decision Process: Responder's Strategy
- 3.1 Model of the Responder
- 3.2 Dynamic Programming in UG
- 4 Illustrative Experiments
- 4.1 Models of Proposer
- 4.2 Experiment Setup
- 4.3 Results
- 5 Conclusions
- References
- Implementing Argumentation-Enabled Empathic Agents
- 1 Introduction
- 2 Empathic Agent Concepts
- 3 Implementation of an Empathic Agent with Jason
- 4 Argumentation for Empathic Agents: Reaching Consensus in Case of Inconsistent Beliefs
- 5 Argumentation Semantics Analysis
- 6 Discussion
- 6.1 Argumentation and Jason
- 6.2 Alternative Negotiation Approaches
- 6.3 Limitations
- 6.4 Towards a Generic Empathic Agent
- 7 Conclusion
- References
- Towards Fully Probabilistic Cooperative Decision Making
- 1 Introduction
- 1.1 Paper Layout
- 1.2 Notions and Notation
- 2 Flat Multi-agents Systems
- 3 Single Agent Using FPD
- 3.1 Formulation and Solution of Fully Probabilistic Design
- 3.2 Certainty-Equivalent Receding-Horizon FPD
- 4 Multiple Agents Sharing Ideal Closed-Loop Models
- 4.1 Cooperation Circumstances
- 4.2 Question Related to Pooling for FPD
- 4.3 Algorithmic Summary
- 5 Experimental Part
- 5.1 Simulation Set Up
- 5.2 Commented Results
- 6 Concluding Remarks
- References
- Endorsement in Referral Networks
- 1 Introduction
- 2 Related Work
- 3 Background
- 3.1 Motivation
- 3.2 Referral Network Preliminaries
- 3.3 Proactive Skill Posting Preliminaries
- 4 Endorsement
- 5 Experimental Setup
- 6 Results
- 6.1 Robustness to Early Failures
- 6.2 Performance Gain
- 6.3 Tolerance to Noisy Self-skill Estimates
- 6.4 Incentive Compatibility
- 6.5 Generalizability of the Endorsement Framework
- 7 Conclusions and Future Work
- References
- Markov Chain Monte Carlo for Effective Personalized Recommendations
- 1 Introduction
- 2 Background and Related Work
- 2.1 Related Work
- 3 A Personalized Recommender System
- 3.1 Constraints
- 3.2 Beliefs Updating
- 3.3 Ranking the Items
- 3.4 Clustering
- 4 System Evaluation
- 4.1 Building the Synthetic Users
- 4.2 Experiments and Results
- 5 Conclusions and Future Work
- References
- Computing Consensus: A Logic for Reasoning About Deliberative Processes Based on Argumentation
- 1 Introduction
- 2 Background on Abstract Argumentation
- 3 Deliberative Dynamic Logic
- 4 Model Checking on Finitary Models
- 5 Conclusion and Future Work
- References
- Decentralized Multiagent Approach for Hedonic Games
- 1 Introduction
- 2 Preliminaries
- 3 Related Work
- 4 Methods
- 4.1 Decentralized Algorithm for Hedonic Games
- 4.2 Budding
- 4.3 Adapting the Decentralized Approach for Constrained Coalition Formation
- 5 Experiments
- 5.1 Unconstrained Coalition Hedonic Games
- 5.2 Constrained Coalition Hedonic Games
- 6 Results and Discussion
- 6.1 Results in Unconstrained Coalition Hedonic Games
- 6.2 Results in Constrained Coalition Hedonic Games
- 7 Conclusion
- References
- Deep Reinforcement Learning in Strategic Board Game Environments
- 1 Introduction
- 2 Background and Related Work
- 2.1 Deep Reinforcement Learning
- 2.2 Action-Dependent Features and Q-Decomposition
- 2.3 The Settlers of Catan (SoC) Domain
- 3 Our Approach
- 3.1 Local Q-Function Approximation
- 3.2 Deep Architecture
- 3.3 The DRRL Agent
- 4 Evaluation
- 4.1 Domain State Representation and Action Set
- 4.2 DRRL in the SoC Domain
- 4.3 Simulations and Results
- 5 Conclusions and Future Work
- References
- Counterfactually Fair Prediction Using Multiple Causal Models
- 1 Introduction
- 2 Background
- 2.1 Causality
- 2.2 Fairness
- 2.3 Opinion Pooling
- 3 Aggregation of Causal Models Under Fairness
- 3.1 Problem Formalization
- 3.2 Qualitative Aggregation over the Graph
- 3.3 .3em plus .1em minus .1emQuantitative Aggregation over the Distribution of the Predictor
- 3.4 Illustration
- 4 Conclusion
- References
- Author Index
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