Maurice Pagnucco

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Abstract. The problem of how to remove information from an agent’s stock of beliefs is of paramount concern in the belief change literature. An inquiring agent may remove beliefs for a variety of reasons: a belief may be called into doubt or the agent may simply wish to entertain other possiblities. In the prominent AGM framework [1, 8] for belief change,(More)
insightful comments on an earlier draft of this paper. I thank the referees for Erkenntnis who, apart from giving extended commen ts and suggestions, provided me with some hard-to-nd relevant material. I also thank Williams for their suggestions. The errors that remain are, of course, m ine. Abstract In this paper it is argued that, in order to solve the(More)
The ability to reason about action and change has long been considered a necessary component for any intelligent system. Many proposals have been offered in the past to deal with this problem. In this paper, we offer a new approach to belief change associated with performing actions that addresses some of the shortcomings of these approaches. In particular,(More)
This paper considers the problem of composing or scheduling several (non-deterministic) behaviors so as to conform to a specified target behavior as well as satisfying constraints imposed by the environment in which the behaviors are to be performed. This problem has already been considered by several works in the literature and applied to areas such as web(More)
1 School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia zqzh390@cse.unsw.edu.au 2 ARC Centre of Excellence for Autonomous Systems and National ICT Australia, School of Comp. Sci. and Eng., The University of New South Wales, Sydney, NSW 2052, Australia morri@cse.unsw.edu.au http://www.cse.unsw.edu.au/(More)
In multi-robot task allocation problems with inschedule dependencies, tasks with high costs have a large influence on the total time required for a team of robots to complete all tasks. We reduce this influence by calculating a novel task cost dispersion value that measures robots’ collective preference for each task. By modifying the winner determination(More)