KER Special Issue on Adaptive and Learning Agents
Guest Editors: Peter Vrancx, Enda Howley and Matt Knudson
The Knowledge Engineering Review , Cambridge Journals, Volume 31 (Issue 2), Print scheduled for March, 2016.
Introduction
The field of Adaptive Learning Agents studies systems that are capable of acting autonomously and adapting to their surroundings. It encompasses research from disciplines as diverse as Artificial Intelligence, Software Engineering, Biology, as well as Cognitive and Social Sciences.
This special issue contains selected papers from the 2012 Adaptive and Learning Agents (ALA) workshop, held as a satellite workshop at the Autonomous Agents and MultiAgent Systems conference (AAMAS) in Valencia, Spain. The goal of the ALA workshop is to increase awareness and interest in adaptive agent research, encourage collaboration, and provide a representative overview of current research in the area of adaptive learning agents. It aims at bringing together not only different areas of computer science (e.g. agent architectures, reinforcement learning, and evolutionary algorithms), but also different fields studying similar concepts (e.g. game theory, bio-inspired control, and mechanism design). The workshop serves as an interdisciplinary forum for the discussion of ongoing or completed work in adaptive and learning agents and multiagent systems.
Contents
Preprints of the accepted papers can be downloaded below until the print edition of the journal appears.- Peter Vrancx, Enda Howley and Matt Knudson: Preface to the Special Issue.
- Chris HolmesParker, Adrian K. Agogino and Kagan Tumer: Combining Reward Shaping and Hierarchies for Scaling to Large Multiagent Systems.
- David Catteeuw and Bernard Manderick: Honesty and Deception in Populations of Selfish, Adaptive Individuals.
- Kyriakos Efthymiadis, Sam Devlin and Daniel Kudenko: Overcoming Incorrect Knowledge in Plan-Based Reward Shaping.
- Sam Devlin and Daniel Kudenko: Plan-Based Reward Shaping for Multi-Agent Reinforcement Learning.
- Yann-Michael De Hauwere, Sam Devlin, Daniel Kudenko and Ann Nowe: Context-sensitive reward shaping for sparse interaction multi-agent system.
- Abdel Rodriguez, Peter Vrancx and Ann Nowe: A Reinforcement Learning Approach to Coordinate Exploration with Limited Communication in Continuous Action Games.
Return to ALA2012 site or VUB AI Lab main site