The Multi-Agent Programming Contest 2019

Name: The Multi-Agent Programming Contest 2019 | Agents Assemble - Block by Block to Victory
Brand: Springer
Price: 53.49 EUR
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Agents Assemble - Block by Block to Victory

Tobias Ahlbrecht Jürgen Dix Niklas Fiekas Tabajara Krausburg(Herausgeber*in)

Springer (Verlag)

Erschienen am 23. September 2020

VII, 151 Seiten

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978-3-030-59299-8 (ISBN)

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Beschreibung

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Inhalt

Intro
Preface
Organization
Contents
Overview
The Multi-Agent Programming Contest: A Résumé*5pt
1 Introduction
1.1 Structure of This Work
1.2 Agents
1.3 Goals and Purpose of the MAPC
1.4 Related Work
2 The MASSim Framework
2.1 From 2006: The Early Days
2.2 2017 Until Today: Simply Going Forward
3 History and Evolution of the Contest
3.1 Early Phase
3.2 Agents on Mars
3.3 The City Scenario
4 2019: Agents Assemble
4.1 Scenario
4.2 Participants
4.3 Tournament and Results
4.4 Interesting Simulations
4.5 Survey Results
5 Lessons Learned
5.1 Agents Assemble Scenario
5.2 General
6 Challenges and Outlook
References
Contest Entries
LFC: Combining Autonomous Agents and Automated Planning in the Multi-Agent Programming Contest
1 Introduction
2 Software Architecture
2.1 JaCaMo
2.2 Fast Downward
3 Strategies and Behaviours
3.1 Exploration
3.2 Agent Identification
3.3 Goal Evaluation
3.4 Building a Map
3.5 From Exploration to Tasks
3.6 Optimal In-Vision Planning
3.7 Achieving Tasks
4 Match Analysis
4.1 LFC vs FIT BUT
4.2 LFC vs TRG
4.3 LFC vs GOAL-DTU
5 Team Overview: Short Answers
5.1 Participants and Their Background
5.2 Statistics
5.3 Agent System Details
5.4 Scenario and Strategy
5.5 And the Moral of It Is .
6 Conclusion
References
Multi-Agent Programming Contest 2019 FIT BUT Team Solution
1 Introduction
1.1 MAPC 2019 Environment
1.2 DeSouches and FIT BUT Team at MAPC 2019
2 deSouches Multi-agent System
2.1 General deSouches and His Soldiers
2.2 Agent Level Architecture
2.3 Goals
2.4 Synchronization of Agent Groups
2.5 Scenarios
3 FIT BUT System
3.1 Design
3.2 Synchronization
3.3 FIT BUT Agent Reasoning Cycle
3.4 Options Analysis
3.5 Plan Selection
3.6 Action Reservation System
3.7 Task Possibilities Commutations
4 Limitations and Possible Improvements
4.1 Size Control
4.2 Hoarding
5 Conclusion
A Team Overview: Short Answers
A.1 Participants and Their Background
A.2 Statistics
A.3 Agent System Details
A.4 Scenario and Strategy
A.5 And the Moral of It Is .
References
GOAL-DTU: Development of Distributed Intelligence for the Multi-Agent Programming Contest
1 Introduction
2 Agent Programming in GOAL
2.1 The GOAL Execution Loop
2.2 Action Selection
3 Strategy
4 Agent Knowledge
4.1 The Current Position
4.2 Visited Positions
4.3 Positions of Goal Cells
4.4 Blocks, Dispensers and Obstacles
4.5 Attached Blocks
5 Agent Communication and Shared Knowledge
5.1 Encountering Other Agents
5.2 Goal Cells and Agent Positions
5.3 Attached Blocks
6 Agent Movement
6.1 Evaluation Functions
7 Solving Tasks
7.1 Collecting Blocks
7.2 Task Planning
7.3 Executing Task Plans
8 Evaluation of Matches
9 Discussion
9.1 Changes Since the Contest
9.2 Technical Issues During the Contest
9.3 Known Problems and Bugs
9.4 Improvements
10 Conclusion
A Team Overview: Short Answers
A.1 Participants and Their Background
A.2 Statistics
A.3 Agent system details
A.4 Scenario and Strategy
A.5 And the moral of it is .
References
The Requirement Gatherers' Approach to the 2019 Multi-Agent Programming Contest Scenario
1 Introduction
1.1 The 2019 Scenario: Agents Assemble
2 System Design: Challenges and Motivations
2.1 Jason Internal Actions and Their Usage
2.2 A High-Level Overview of Our Approach
2.3 General Software Engineering Concerns
2.4 MAPC Scenario-Specific Design Concerns
3 System Design
3.1 General System Design
3.2 MAPC Scenario-Specific System Design
3.3 Monitoring Agent Attachments
3.4 Task Requirement Planning
4 Debugging
4.1 Jason's Mind Inspector
4.2 IntelliJ IDEA IDE Debugging Support
4.3 Agent Visualization Tool
4.4 The Combined Approach
5 The Team Strategy
5.1 The Operator
5.2 Shared Strategy Components
5.3 Attacker Strategy
5.4 Builder Strategy
6 Agent Performance
6.1 Match Performance Metrics
6.2 General Remarks and Improvements
6.3 Match Performance Results
7 Conclusion
8 Team Overview: Short Answers
8.1 Participants and Their Background
8.2 Statistics
8.3 Agent System Details
8.4 Scenario and Strategy
8.5 And the Moral of It Is .
References
Author Index

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