As Bayesian networks become widely accepted as a normative formalism for diagnosis based on probabilistic knowledge, they are applied to increasingly larger problem domains. These...
Yanping Xiang, Kristian G. Olesen, Finn Verner Jen...
We use reinforcement learning (RL) to compute strategies for multiagent soccer teams. RL may pro t signi cantly from world models (WMs) estimating state transition probabilities an...
Bayesian networks (BNs) have been widely used as a model for knowledge representation and probabilistic inferences. However, the single probability representation of conditional d...
We present Hintikka games for formulae of the probabilistic temporal logic PCTL and countable labeled Markov chains as models, giving an operational account of the denotational se...
Harald Fecher, Michael Huth, Nir Piterman, Daniel ...
We achieve two goals in this paper: (1) to build a novel appearance-based object representation that takes into account variations in contrast often found in training images; (2) ...
Chakra Chennubhotla, Allan D. Jepson, John Midgley