Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. the PAAMS Collection 20th International Conference, PAAMS 2022, l'Aquila, Italy, July 13-15, 2022, Proceedings

This book constitutes the proceedings of the 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022, held in L'Aquila, Italy in July 2022.The 37 full papers in this book were reviewed and selected from 67 submissions. Another 10 demonstrations pape...

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Bibliographic Details
Main Authors Dignum, Frank, Mathieu, Philippe, Corchado, Juan Manuel, De La Prieta, Fernando
Format eBook Conference Proceeding
LanguageEnglish
Published Cham Springer International Publishing AG 2022
Springer International Publishing
Edition1
SeriesLecture Notes in Computer Science
Subjects
Online AccessGet full text

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Table of Contents:
  • 6.2 Learning Agent Selection in MARL-SA -- 7 Conclusion -- References -- Hierarchical Collaborative Hyper-Parameter Tuning -- 1 Introduction -- 2 Methodology -- 2.1 Agent-Based Hyper-Parameter Tuning -- 2.2 Guided Randomized Agent-Based Tuning Algorithm -- 3 Results and Discussion -- 4 Conclusion -- References -- Towards the Combination of Model Checking and Runtime Verification on Multi-agent Systems -- 1 Introduction -- 2 Preliminaries -- 2.1 Models for Multi-agent Systems -- 2.2 Syntax -- 2.3 Semantics -- 2.4 Runtime Verification and Monitors -- 2.5 Negative and Positive Sub-models -- 3 Our Procedure -- 4 Our Tool -- 4.1 Experiments -- 5 Conclusions and Future Work -- References -- Explaining Semantic Reasoning Using Argumentation -- 1 Introduction -- 2 Background -- 2.1 Agent Oriented Programming Languages -- 2.2 Argumentation Schemes -- 2.3 OWL Ontologies -- 3 Scenario -- 4 Querying Ontologies -- 5 Translating SWRL Rules into Argumentation Schemes -- 5.1 Translating Arguments to Natural Language Explanations -- 6 Related Work and Conclusions -- References -- How to Solve a Classification Problem Using a Cooperative Tiling Multi-agent System? -- 1 Introduction -- 2 Related Work -- 2.1 Aggregation of Classifiers -- 2.2 Multi-agent Systems -- 3 Smapy -- 3.1 General Principle -- 3.2 Agents -- 3.3 Feedback -- 3.4 Non-cooperative Situations -- 4 Comparison of Linear Classifiers Alone with Context Learning Approach -- 4.1 Input Data -- 4.2 Experimental Protocol -- 5 Results -- 5.1 Classification Accuracy -- 5.2 Decision Boundaries -- 6 Discussion -- 7 Conclusion -- References -- Multi-agent Learning of Numerical Methods for Hyperbolic PDEs with Factored Dec-MDP -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Factored Dec-MDP -- 3.2 Hyperbolic PDEs and WENO Scheme -- 4 Problem Formulation and Analysis
  • Agent-Based Modelling and Simulation of Decision-Making in Flying Ad-Hoc Networks -- 1 Introduction -- 2 Motivation -- 2.1 System Overview -- 2.2 ConOps Agent Algorithm -- 3 Agent-Based Modelling and Simulation -- 3.1 Implementation and Usage -- 3.2 Evaluation Method -- 3.3 Evaluation Results -- 4 Conclusion -- References -- Deep RL Reward Function Design for Lane-Free Autonomous Driving -- 1 Introduction -- 2 Background and Related Work -- 2.1 Deep Deterministic Policy Gradient -- 2.2 Related Work -- 3 Our Approach -- 3.1 The Lane-Free Traffic Environment -- 3.2 State Representation -- 3.3 Action Space -- 3.4 Reward Function Design -- 4 Experimental Evaluation -- 4.1 RL Algorithm and Simulation Setup -- 4.2 Results and Analysis -- 5 Conclusions and Future Work -- References -- An Emotion-Inspired Anomaly Detection Approach for Cyber-Physical Systems Resilience -- 1 Introduction -- 2 Related Work -- 2.1 Resilience -- 2.2 Anomaly Detection for CPS Resilience -- 3 The Proposed Approach -- 4 Experiments -- 4.1 System Description -- 4.2 Measures of Resilience -- 4.3 Scenario Description -- 4.4 Results and Evaluation -- 5 Conclusions and Perspectives -- References -- Bundle Allocation with Conflicting Preferences Represented as Weighted Directed Acyclic Graphs -- 1 Introduction -- 2 Problem Model -- 3 Path Allocation Schemes -- 3.1 Utilitarian Allocation (util) -- 3.2 Leximin Allocation (lex) -- 3.3 Approximated Leximin Allocation (a-lex) -- 3.4 Greedy Allocation (greedy) -- 3.5 Round-Robin Allocations (p-rr and n-rr) -- 4 Experimental Evaluation -- 5 Conclusion -- References -- Shifting Reward Assignment for Learning Coordinated Behavior in Time-Limited Ordered Tasks -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 3.1 Agent and Environment -- 3.2 Task Structure -- 3.3 Time Limitation for Completing Each Task -- 4 Proposed Method
  • 4.1 Shifting Two-Stage Reward Assignment
  • 4.1 Numerical Methods as Multi-agent Systems -- 4.2 Analysis of Different Reward Formulations -- 5 Experiment Results -- 5.1 Euler Equations and Training Setup -- 5.2 Results and Discussions -- 6 Conclusion -- References -- Multi-agent-based Structural Reconstruction of Dynamic Topologies for Urban Lighting -- 1 Introduction -- 2 Real-World and Simulated Environment -- 3 Objective and Methods -- 3.1 Agent Modelling and Formalisation -- 3.2 Local Neighborhood Discrimination -- 3.3 Evaluation Metrics -- 4 Results and Discussion -- 5 Conclusion -- References -- Control Your Virtual Agent in its Daily-activities for Long Periods -- 1 Introduction -- 2 Related Work -- 3 Agent Model Description -- 3.1 Global Model Structure -- 3.2 Agent Internal State -- 3.3 Decision-Making Model -- 3.4 Task Execution Model -- 4 Results -- 5 Conclusion -- References -- Multi-Agent Task Allocation Techniques for Harvest Team Formation -- 1 Introduction -- 2 Background -- 3 Approach -- 3.1 Problem Description -- 3.2 Solution Fitness -- 3.3 GA Approach -- 3.4 Auction Approach -- 4 Experiments -- 4.1 Data -- 4.2 Metrics -- 5 Results -- 5.1 Trade-off Between Staff Time and Execution Time -- 5.2 Comparison to Alternative Approaches -- 6 Summary and Future Work -- References -- Study of Heterogeneous User Behavior in Crowd Evacuation in Presence of Wheelchair Users -- 1 Introduction -- 2 Related Works -- 3 Our Proposed Agent-Based Panic Model Involving Wheelchair Users -- 3.1 Agent Attributes -- 3.2 Agent Model -- 3.3 Agent Behavior Logic -- 4 Experimental Setup -- 4.1 Agent Parameters -- 4.2 Environmental Parameters -- 4.3 Performance Metrics -- 5 Performance Evaluation and Results -- 5.1 Rate of Evacuation -- 5.2 Agent Attribute Distribution Versus Exit Time -- 5.3 Correlation Coefficient Trend -- 6 Conclusion and Future Works -- References
  • Intro -- Preface -- Organization -- Contents -- Main Track -- An Open MAS/IoT-Based Architecture for Large-Scale V2G/G2V -- 1 Introduction -- 2 Background and Related Work -- 3 System Architecture -- 4 Agent Interactions -- 4.1 Implemented Agent Strategies -- 5 Experimental Evaluation -- 5.1 Simulating Algorithms and Mechanisms -- 6 Conclusions and Future Work -- References -- .26em plus .1em minus .1emInvestigating Effects of Centralized Learning Decentralized Execution on Team Coordination in the Level Based Foraging Environment as a Sequential Social Dilemma -- 1 Introduction -- 2 Background -- 2.1 Multi Agent Reinforcement Learning (MARL) -- 2.2 Sequential Social Dilemmas -- 2.3 Centralized Learning Decentralized Execution (CLDE) -- 3 Related Work -- 3.1 MARL Coordination in SSDs -- 3.2 Learning Algorithms -- 4 LBF as a SSD -- 5 Experimental Design -- 6 Results -- 7 Conclusion -- References -- Agent Based Digital Twin of Sorting Terminal to Improve Efficiency and Resiliency in Parcel Delivery -- 1 Introduction -- 2 Problem Statement -- 2.1 State of the Art Analysis Techniques -- 3 Approach -- 3.1 Agent Based Realization -- 3.2 Simulation-Led Experimentation Aid -- 4 Illustrative Case Study -- 5 Conclusion -- References -- Fully Distributed Cartesian Genetic Programming -- 1 Introduction -- 2 Distributed Cartesian Genetic Programming -- 3 Results -- 3.1 Regression -- 3.2 N-Parity -- 3.3 Classification -- 4 Conclusion -- References -- Data Synchronization in Distributed Simulation of Multi-Agent Systems -- 1 Introduction -- 2 Data Synchronization in Distributed MAS Simulations -- 3 Synchronization Modes -- 3.1 Read and Write Operations -- 3.2 Data Synchronization Interface -- 3.3 Specification of Proposed Modes -- 3.4 Properties -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Results -- 5 Conclusion -- References
  • Co-Learning: Consensus-based Learning for Multi-Agent Systems -- 1 Introduction -- 2 Co-Learning Algorithm -- 2.1 Consensus-based Multi-Agent Systems -- 2.2 Algorithm Description -- 3 Validation of Co-Learning Algorithm -- 3.1 Convergence Analysis -- 3.2 Network Efficiency -- 3.3 Effect of Network Size -- 4 Execution Using SPADE Agents -- 4.1 Co-Learning in SPADE -- 4.2 Execution Example -- 5 Conclusions -- References -- Multiagent Pickup and Delivery for Capacitated Agents -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 4 Method -- 4.1 TPMT -- 4.2 TPMC -- 5 Evaluation -- 5.1 Case Studies -- 5.2 Experimental Setup -- 5.3 Results -- 6 Conclusion -- References -- Using Institutional Purposes to Enhance Openness of Multi-Agent Systems -- 1 Introduction -- 2 Artificial Institutions and Purposes -- 3 Implementing a Multi-agent System with and Without the Purpose Model -- 3.1 Implementation Without Institutions and Purposes -- 3.2 Implementation with Institutions and Purposes -- 4 Discussion About both Implementations -- 5 Conclusions and Future Work -- References -- Developing BDI-Based Robotic Systems with ROS2 -- 1 Introduction -- 2 Background -- 3 A Multi-agent Robotic RT-BDI Architecture -- 3.1 Development Tool-Kit -- 4 Demonstration of the MA-RT-BDI Architecture -- 5 Related Work -- 6 Conclusions and Future Work -- References -- Combining Multiagent Reinforcement Learning and Search Method for Drone Delivery on a Non-grid Graph -- 1 Introduction -- 2 Model -- 2.1 Problem Definition -- 2.2 Formulate Problem as Dec-MDP -- 3 Related Work -- 3.1 Search Methods -- 3.2 Dynamic Programming Methods -- 4 Algorithm -- 4.1 The Search Module -- 4.2 The MARL Module -- 4.3 The Training Process -- 5 Evaluation -- 5.1 Evaluation Settings -- 5.2 Evaluation Results -- 6 Discussion -- 6.1 The MARL-SA Position in MAPF Solutions