Abstract. We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated acc...
Paolo Viappiani, Sandra Zilles, Howard J. Hamilton...
This paper extends the framework of dynamic influence diagrams (DIDs) to the multi-agent setting. DIDs are computational representations of the Partially Observable Markov Decisio...
The Partially Observable Markov Decision Process (POMDP) model is explored for high level decision making for Unmanned Air Vehicles (UAVs). The type of UAV modeled is a flying mun...
In this paper we describe a technique to infer the topology and connectivity information of a network of cameras based on observed motion in the environment. While the technique c...
We present a vision based, adaptive, decision theoretic model of human facial displays in interactions. The model is a partially observable Markov decision process, or POMDP. A POM...