PUB - Publikationen an der Universität Bielefeld

In imitation learning, agents are trained to carry out certain actions by examining a demonstration of the task at hand. Though common in robotics, little work has been done in translating these concepts to computer games. Given that present-day games generally use antiquated AI techniques which can often lead to stilted, mechanical and conspicuously artificial behaviour, it seems likely that approaches based on the imitation of human players may produce agents which convey a more humanlike impression than their traditional counterparts. At the same time, there exists no formal method of quantifying what constitutes a 'humanlike' impression; an equivalent of the Turing test is needed, with the requirement that an agent's appearance and behaviour be capable of deceiving an observer into misidentifying it as human. The aims of this paper are thus threefold; we describe an approach to the imitation of strategic behaviour and motion, propose a formal method of quantifying the degree to which different agents are perceived as 'humanlike', and present the results of a series of experiments using these two systems.