{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":23,"n_cols":5,"rows_kept":23,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[10,2,10,3],[11,2,11,3],[14,2,14,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996495570103316"]
[null,null,"Titolo",null,"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 / / edited by Frank Dignum [and three others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-18192-1"]
[null,null,"Descrizione fisica",null,"1 online resource (529 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.13616"]
[null,null,"Disciplina",null,"006.22"]
[null,null,"Soggetti",null,"Cooperating objects (Computer systems)"]
[{"$nulls":4},"Multiagent systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Conclusion -- References -- Data Synchronization in Distributed Simulation of Multi-Agent Systems -- 1 Introduction -- 2 Data Synchronization in Distributed MAS Simulations -- 3 Synchronization Modes -- 3.1 Read and Write Operations -- 3.2 Data Synchronization Interface -- 3.3 Specification of Proposed Modes -- 3.4 Properties -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Results -- 5 Conclusion -- References."]
[{"$nulls":4},"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."]
[{"$nulls":4},"6.2 Learning Agent Selection in MARL-SA -- 7 Conclusion -- References -- Hierarchical Collaborative Hyper-Parameter Tuning -- 1 Introduction -- 2 Methodology -- 2.1 Agent-Based Hyper-Parameter Tuning -- 2.2 Guided Randomized Agent-Based Tuning Algorithm -- 3 Results and Discussion -- 4 Conclusion -- References -- Towards the Combination of Model Checking and Runtime Verification on Multi-agent Systems -- 1 Introduction -- 2 Preliminaries -- 2.1 Models for Multi-agent Systems -- 2.2 Syntax -- 2.3 Semantics -- 2.4 Runtime Verification and Monitors -- 2.5 Negative and Positive Sub-models -- 3 Our Procedure -- 4 Our Tool -- 4.1 Experiments -- 5 Conclusions and Future Work -- References -- Explaining Semantic Reasoning Using Argumentation -- 1 Introduction -- 2 Background -- 2.1 Agent Oriented Programming Languages -- 2.2 Argumentation Schemes -- 2.3 OWL Ontologies -- 3 Scenario -- 4 Querying Ontologies -- 5 Translating SWRL Rules into Argumentation Schemes -- 5.1 Translating Arguments to Natural Language Explanations -- 6 Related Work and Conclusions -- References -- How to Solve a Classification Problem Using a Cooperative Tiling Multi-agent System? -- 1 Introduction -- 2 Related Work -- 2.1 Aggregation of Classifiers -- 2.2 Multi-agent Systems -- 3 Smapy -- 3.1 General Principle -- 3.2 Agents -- 3.3 Feedback -- 3.4 Non-cooperative Situations -- 4 Comparison of Linear Classifiers Alone with Context Learning Approach -- 4.1 Input Data -- "]
[{"$nulls":4},"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."]
[{"$nulls":4},"4.1 Numerical Methods as Multi-agent Systems -- 4.2 Analysis of Different Reward Formulations -- 5 Experiment Results -- 5.1 Euler Equations and Training Setup -- 5.2 Results and Discussions -- 6 Conclusion -- References -- Multi-agent-based Structural Reconstruction of Dynamic Topologies for Urban Lighting -- 1 Introduction -- 2 Real-World and Simulated Environment -- 3 Objective and Methods -- 3.1 Agent Modelling and Formalisation -- 3.2 Local Neighborhood Discrimination -- 3.3 Evaluation Metrics -- 4 Results and Discussion -- 5 Conclusion -- References -- Control Your Virtual Agent in its Daily-activities for Long Periods -- 1 Introduction -- 2 Related Work -- 3 Agent Model Description -- 3.1 Global Model Structure -- 3.2 Agent Internal State -- 3.3 Decision-Making Model -- 3.4 Task Execution Model -- 4 Results -- 5 Conclusion -- References -- Multi-Agent Task Allocation Techniques for Harvest Team Formation -- 1 Introduction -- 2 Background -- 3 Approach -- 3.1 Problem Description -- 3.2 Solution Fitness -- 3.3 GA Approach -- 3.4 Auction Approach -- 4 Experiments -- 4.1 Data -- 4.2 Metrics -- 5 Results -- 5.1 Trade-off Between Staff Time and Execution Time -- 5.2 Comparison to Alternative Approaches -- 6 Summary and Future Work -- References -- Study of Heterogeneous User Behavior in Crowd Evacuation in Presence of Wheelchair Users -- 1 Introduction -- 2 Related Works -- 3 Our Proposed Agent-Based Panic Model Involving Wheelchair Users -- 3.1 Agent Attributes -- 3.2 Agent Model -- 3.3 Agent Behavior Logic -- 4 Experimental Setup -- 4.1 Agent Parameters -- 4.2 Environmental Parameters -- 4.3 Performance Metrics -- 5 Performance Evaluation and Results -- 5.1 Rate of Evacuation -- 5.2 Agent Attribute Distribution Versus Exit Time -- 5.3 Correlation Coefficient Trend -- 6 Conclusion and Future Works -- References."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"-- Shifting Reward Assignment for Learning Coordinated Behavior in Time-Limited Ordered Tasks -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 3.1 Agent and Environment -- 3.2 Task Structure -- 3.3 Time Limitation for Completing Each Task -- 4 Proposed Method."]
[{"$nulls":4},"4.1 Shifting Two-Stage Reward Assignment."]