
The Multi-Agent Programming Contest 2019
Description
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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.
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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
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