PRIMA 2024: Principles and Practice of Multi-Agent Systems
Description
This book constitutes the refereed proceedings of the 25th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2024, held in Kyoto, Japan, during November 18-24, 2024.
The 23 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 76 submissions. They are organized in the following topical sections: coordination and cooperation; market approaches; logics; learning; agent-based modelling and simulation; computational social choice.
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Content
.- Coordination and Cooperation.
.- Attila: a Negotiating Diplomacy Player Based on Purely Symbolic A.I..
.- Automated Negotiation in Supply Chains: A generalist environment for RL/MARL Research.
.- Bargaining through Amalgamation.
.- Cascading Power Reduction in Wi-Fi Networks: A Cooperative Approach to Enhance Performance and Efficiency.
.- Modelling the Dynamics of Identity and Fairness in Allocation Game.
.- Path Finding with Flexible Provisional Booking in Multi-Agent Pickup and Delivery Problems.
.- H-EFP: Bridging Efficiency in Multi-Agent Epistemic Planning with Heuristics.
.- SPPP: Stochastic penalty path planning for MAPF under conditions of uncertain travel time.
.- Market Approaches.
.- A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets -- A New Microfoundations of GARCH model.
.- Modelling Congestion and Price Competition in EV Charging Markets.
.- What is the Price for Lending in Financial Networks?.
.- Equilibria of Carbon Allowance Auctions: Emissions and Productivity.
.- Logics.
.- An action language-based formalisation of an abstract argumentation framework.
.- Belief Reconfiguration Without Oracle.
.- A Proof Calculus for Ethical Reasoning.
.- Resource Action-based Bounded ATL: a New Logic for MAS to express a cost over the actions.
.- Judgment Aggregation with Unknown Variable Reliability.
.- Theory and Practice of Quantitative ATL.
.- Added Value of Probabilistic Model Checking of vGOAL for Autonomous Systems .
.- Learning.
.- Improving Reinforcement Learning-based Autonomous Agents with Causal Models.
.- Incentive-driven Multi-Agent Reinforcement Learning Approach for Commons Dilemmas in Land-Use.
.- Robust Individualistic Learning in Many-Agent Systems.
.- Discarding Erroneous Knowledge Online in Transfer Reinforcement Learning.
.- Efficient Preference Elicitation in Iterative Combinatorial Auctions with Many Participants.
.- Safe Multi-agent Reinforcement Learning for Drone Routing Problems.
.- Explainability and Interpretability of an Ensemble Multi-Agent System for Supervised Learning.
.- VickreyFeedback: Cost-efficient Data Construction for Reinforcement Learning from Human Feedback.
.- Agent-based Modelling and Simulation.
.- An Agent-Based Model to Simulate the Effects of Tsunami Warnings on Pedestrian Evacuation : Sensitivity Analysis and Early Findings.
.- Beyond Chatbots: Enhancing Luxembourgish Language Learning Through Multi-Agent Systems and Large Language Model.
.- Computational Social Choice.
.- Multi-Stage Generalized Deferred Acceptance Mechanism: Strategyproof Mechanism for Handling General Hereditary Constraints.
.- Friend- and Enemy-oriented Hedonic Games with Strangers.
.- Taming Dilation in Imprecise Pooling.
.- The Score Reveal Problem: How do we Maximise Entertainment?.
