The Multi-Agent Programming Contest 2021: One-and-a-Half Decades of Exploring Multi-Agent Systems: 12947 (Lecture Notes in Computer Science, 12947)

✍ Scribed by Tobias Ahlbrecht (editor), Jürgen Dix (editor), Niklas Fiekas (editor), Tabajara Krausburg (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 164
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

This book constitutes the 15th edition of the annual Multi-Agent Programming Contest, MAPC 2020. 

It gives an overview of the competition, describes the current scenario. Furthermore, it summarises this year's participants and their approaches and analyses some of the matches played and the contest as a whole. In the second part, each team contributed a paper describing their approach and experiences with creating a team of agents to participate in the contest. 

✦ Table of Contents

Preface
Organization
Contents
Overview
The 15th Multi-Agent Programming Contest
1 Introduction
1.1 Related Work and Competitions
1.2 Outline
2 The Current Setting
2.1 Challenges
2.2 Modifications
3 The Tournament
3.1 Overall Organization
3.2 Participants
3.3 Final Ranking
3.4 Team Performance
3.5 Selected Matches
3.6 Free-for-All
4 Lessons Learned
4.1 A New Version of the Agents Assemble Scenario
4.2 The 15th MAPC
5 Outlook
References
Participants
FIT BUT: Rational Agents in the Multi-Agent Programming Contest
1 Introduction
2 System Design
2.1 Strategies
2.2 Synchronization
2.3 Constructing a Map
2.4 Agent Reasoning Cycle
2.5 Goals and Plans
2.6 Action Reservation System
3 Summary of Matches
4 Conclusion
5 Limitations and Possible Improvements
A Team Overview: Short Answers
A.1 Participants and Their Background
A.2 Statistics
A.3 Technology and Techniques
A.4 Agent System Details
A.5 Scenario and Strategy
A.6 And the Moral of it is …
References
The 15th Edition of the Multi-Agent Programming Contest - The GOAL-DTU Team
1 Introduction
2 The Strategy of Our Agents
2.1 Exploration of the Map
2.2 Accepting and Submitting Tasks
3 Storing and Maintaining Information
3.1 Immutable Objects in the Environment
3.2 Agreeing on Coordinates
3.3 Inferring the Map Dimensions
4 Moving About in the Environment
4.1 General Map Exploration
4.2 Movement Towards a Fixed Position
5 Communication Between Agents
5.1 Connecting to Other Agents
6 Constructing and Executing Task Plans
6.1 Construction of Task Plans
6.2 Execution of Task Plans
7 Evaluation of Matches
7.1 GOAL-DTU vs. LTI-USP
7.2 GOAL-DTU vs. MLFC
7.3 GOAL-DTU vs. FIT-BUT
7.4 GOAL-DTU vs. JaCaMo Builders
7.5 Free for All
8 Discussion
8.1 System Robustness
8.2 Technical Issues
8.3 Further Work
9 Conclusion
A Team Overview: Short Answers
A.1 Participants and Their Background
A.2 Statistics
A.3 Technology and Techniques
A.4 Agent System Details
A.5 Scenario and Strategy
A.6 And the Moral of it is …
References
MLFC: From 10 to 50 Planners in the Multi-Agent Programming Contest
1 Introduction
2 Languages and Tools
3 Main Strategies Taken from the 14th MAPC
3.1 Agent Identification
3.2 Building a Map
3.3 Planning
4 New Strategies for the 15th MAPC
4.1 Cartography
4.2 Formal Verification of Map Merging
4.3 Plan Cache
4.4 Bullies
4.5 Achieving Tasks
5 Match Analysis
6 Team Overview: Short Answers
6.1 Participants and Their Background
6.2 Statistics
6.3 Technology and Techniques
6.4 Agent System Details
6.5 Scenario and Strategy
6.6 And the Moral of it is …
7 Conclusion
References
The LTI-USP Strategy to the 2020/2021 Multi-Agent Programming Contest
1 Introduction
2 The 2020/2021 Scenario: Agents Assemble II
3 System Design
3.1 Exploration
3.2 Agent Identification
3.3 Task Owners and Auxiliary Agents
3.4 Task Selection
3.5 Path Planning
3.6 Achieving Tasks After Unexpected Events
4 Match Analysis
4.1 LTI-USP vs. GOAL-DTU
4.2 LTI-USP vs. JaCaMo Builders
4.3 LTI-USP vs. FIT BUT
4.4 LTI-USP vs. MLFC
5 Conclusion
6 Team Overview: Short Answers
6.1 Participants and Their Background
6.2 Statistics
6.3 Technology and Techniques
6.4 Agent System Details
6.5 Scenario and Strategy
6.6 And the Moral of it is …
References
JaCaMo Builders: Team Description for the Multi-agent Programming Contest 2020/21
1 Introduction
2 System Analysis and Design
2.1 Environment Dimension
2.2 Agent Dimension
3 Software Architecture
4 Strategies, Details and Statistics
4.1 Team Strategies
4.2 Comparison to Other Teams
5 Developing and Improving Agents
5.1 Unit and AB Tests for Agents
5.2 Reinforcement Learning with MAB
5.3 Interactive Programming Support
6 Conclusion
A Team Overview: Short Answers
A.1 Participants and Their Background
A.2 Statistics
A.3 Technology and Techniques
A.4 Agent System Details
A.5 Scenario and Strategy
A.6 And the Moral of It Is …
References
Author Index