The Multi-Agent Programming Contest 2019
Agents Assemble - Block by Block to Victory
Published on 23. September 2020
VII, 151 pages
978-3-030-59299-8 (ISBN)
Description
This book constitutes the 14th edition of the annual Multi-Agent Programming Contest, MAPC 2019, and presents its participants.
The 2019 scenario and all its changes from previous competitions are described in the first contribution, together with a brief description and analysis of the five participating teams and a closer look at the matches. It is followed by a contribution from each team, introducing their methods and tools used to create their agent team and analyzing their performance and the contest.
More details
Series
Edition
1st ed. 2020
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Product notice
Reflowable
Illustrations
VII, 151 p. 74 illus., 66 illus. in color.
ISBN-13
978-3-030-59299-8 (9783030592998)
DOI
10.1007/978-3-030-59299-8
Schweitzer Classification
Other editions
Additional editions
Tobias Ahlbrecht | Jürgen Dix | Niklas Fiekas
The Multi-Agent Programming Contest 2019
Agents Assemble - Block by Block to Victory
Book
09/2020
Springer
€53.49
Shipment within 7-9 days
Content
- 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
