In probabilistic reasoning, the problems of existence and identity are important to many different queries; for example, the probability that something that fits some description...
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...
The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-...
We present a general, consistency-based framework for belief change. Informally, in revising K by , we begin with and incorporate as much of K as consistently possible. Formally, ...
Automated problem solving is viewed typically as the expenditure of computation to solve one or more problems passed to a reasoning system. In response to each problem received, e...