57 research outputs found
Belief and Truth in Hypothesised Behaviours
There is a long history in game theory on the topic of Bayesian or "rational"
learning, in which each player maintains beliefs over a set of alternative
behaviours, or types, for the other players. This idea has gained increasing
interest in the artificial intelligence (AI) community, where it is used as a
method to control a single agent in a system composed of multiple agents with
unknown behaviours. The idea is to hypothesise a set of types, each specifying
a possible behaviour for the other agents, and to plan our own actions with
respect to those types which we believe are most likely, given the observed
actions of the agents. The game theory literature studies this idea primarily
in the context of equilibrium attainment. In contrast, many AI applications
have a focus on task completion and payoff maximisation. With this perspective
in mind, we identify and address a spectrum of questions pertaining to belief
and truth in hypothesised types. We formulate three basic ways to incorporate
evidence into posterior beliefs and show when the resulting beliefs are
correct, and when they may fail to be correct. Moreover, we demonstrate that
prior beliefs can have a significant impact on our ability to maximise payoffs
in the long-term, and that they can be computed automatically with consistent
performance effects. Furthermore, we analyse the conditions under which we are
able complete our task optimally, despite inaccuracies in the hypothesised
types. Finally, we show how the correctness of hypothesised types can be
ascertained during the interaction via an automated statistical analysis.Comment: 44 pages; final manuscript published in Artificial Intelligence (AIJ
Predictive Model for Human-Unmanned Vehicle Systems
Advances in automation are making it possible for a single operator to control multiple unmanned vehicles. However, the complex nature of these teams presents a difficult and exciting challenge for designers of humanâunmanned vehicle systems. To build such systems effectively, models must be developed that describe the behavior of the humanâunmanned vehicle team and that predict how alterations in team composition and system design will affect the systemâs overall performance. In this paper, we present a method for modeling humanâunmanned vehicle systems consisting of a single operator and multiple independent unmanned vehicles. Via a case study, we demonstrate that the resulting models provide an accurate description of observed human-unmanned vehicle systems. Additionally, we demonstrate that the models can be used to predict how changes in the human-unmanned vehicle interface and the unmanned vehiclesâ autonomy alter the systemâs performance.Lincoln Laborator
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