5 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
Goal recognition and deception in path-planning
This thesis argues that investigation of goal recognition and deception in the much studied and well-understood context of path-planning reveals nuances to both problems that have previously gone unnoticed. Contemporary goal recognition systems rely on examination of multiple observations to calculate a probability distribution across goals. The first part of this thesis demonstrates that a distribution with identical rankings to current stateof-the-art can be achieved without any observations apart from a known starting point (such as a door or gate) and where the agent is now. It also presents a closed formula to calculate a radius around any goal of interest within which that goal is guaranteed to be the most probable, without having to calculate any actual probability values. In terms of deception, traditionally there are two strategies: dissimulation (hiding the true) and simulation (showing the false). The second part of this thesis shows that current state-of-the-art goal recognition systems do not cope well with dissimulation that does its work by ‘dazzling’ (i.e., obfuscating with hugely suboptimal plans). It presents an alternative, self-modulating formula that modifies its output when it encounters suboptimality, seeming to ‘know that it does not know’ instead of ‘keep changing its mind’. Deception is often regarded as a ‘yes, no’ proposition (either the target is deceived or they are not). Furthermore, intuitively, deceptive path-planning involves suboptimality and must, therefore, be expensive. This thesis, however, presents a model of deception for path-planning domains within which it is possible (a) to rank paths by their potential to deceive and (b) to generate deceptive paths that are ‘optimally deceptive’ (i.e., deceptive to the maximum extent at the lowest cost)