Bayes belief networks and influence diagrams are tools for constructing coherent probabilistic representations of uncertain expert opinion. The construction of such a network with about 30 nodes is used to illustrate a variety of techniques which can facilitate the process of structuring and quantifying uncertain relationships. These include some generalizations of the "noisy OR gate" concept. ...

Learning from demonstration utilizes human expertise to program a robot. We believe this approach to robot programming will facilitate the development and deployment of general purpose personal robots that can adapt to specific user preferences. Demonstrations can potentially take place across a wide variety of environmental conditions. In this paper we study the impact that the users visual ac...

This paper discusses the use of uncertain knowledge in eipert systems. The discussion is focused on the use of these systems to support decision making in complei, uncertain environments. Within this conteit, our research indicates that strictly probabilistic representations, which enable the use or decision-theoretic reasoning, are highly preferable to recently proposed alternatives. Furthermo...

This paper presents a cross-cultural comparison of U.S., Canadian-Anglophone, Canadian-Francophone, and Mexican managers’ attitudes toward upward influence strategies. Generally, it was found that all four groups have similar perspectives in terms of the relative acceptability of various influence strategies. However, examination of absolute ratings of influence behaviors suggests that Canadian...

Modeling the perceived behaviors of other agents improves the performance of an agent in multiagent interactions. We utilize the language of interactive influence diagrams to model repeated interactions between the agents, and ascribe procedural models to other agents. Procedural models offer the benefit of understanding how others arrive at their behaviors. As model spaces are often bounded, t...