6 research outputs found
Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems
Much research in artificial intelligence is concerned with the development of
autonomous agents that can interact effectively with other agents. An important
aspect of such agents is the ability to reason about the behaviours of other
agents, by constructing models which make predictions about various properties
of interest (such as actions, goals, beliefs) of the modelled agents. A variety
of modelling approaches now exist which vary widely in their methodology and
underlying assumptions, catering to the needs of the different sub-communities
within which they were developed and reflecting the different practical uses
for which they are intended. The purpose of the present article is to provide a
comprehensive survey of the salient modelling methods which can be found in the
literature. The article concludes with a discussion of open problems which may
form the basis for fruitful future research.Comment: Final manuscript (46 pages), published in Artificial Intelligence
Journal. The arXiv version also contains a table of contents after the
abstract, but is otherwise identical to the AIJ version. Keywords: autonomous
agents, multiagent systems, modelling other agents, opponent modellin
Evaluating the Effects on Monte Carlo Tree Search of Predicting Co-operative Agent Behaviour
This thesis explores the effects of including an agent-modelling strategy into Monte-Carlo Tree Search. This is to explore how the effects of such modelling might be used to increase the performance of agents in co-operative environments such as games.
The research is conducted using two applications. The first is a co-operative 2-player puzzle game in which a perfect model outperforms an agent that makes the assumption the other agent plays randomly. The second application is the partially observable co-operative card game Hanabi, in which the predictor variant is able to outperform both a standard variant of MCTS and a version that assumes a fixed-strategy for the paired agents. This thesis also investigates a technique for learning player strategies off-line based on saved game logs for use in modelling