We describe a novel integration of Planning with Probabilistic State Estimation and Execution resulting in a unified representational and computational framework based on declarat...
Conor McGann, Frederic Py, Kanna Rajan, John Ryan,...
Recently defined resolution calculi for Max-SAT and signed Max-SAT have provided a logical characterization of the solving techniques applied by Max-SAT and WCSP solvers. In this...
We present the first planner capable of reasoning with both the full semantics of PDDL2.1 (level 3) temporal planning and with numeric resources. Our planner, CRIKEY3, employs heu...
We present a new algorithm for reasoning in the description logic SHIQ, which is the most prominent fragment of the Web Ontology Language OWL. The algorithm is based on ordered bi...
To be effective, an agent that collaborates with humans needs to be able to learn new tasks from humans they work with. This paper describes a system that learns executable task m...
James F. Allen, Nathanael Chambers, George Ferguso...
Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, the problems of interest become increasingly challenging and complex. Makin...
Ming-Wei Chang, Lev-Arie Ratinov, Nicholas Rizzolo...
This work addresses the problem of efficiently learning action schemas using a bounded number of samples (interactions with the environment). We consider schemas in two languages-...
bstract or nonliving entities act or are described as living. And living things gain extra benefits such as animals talking. For this reason, the standard scripts are modified to a...
Agents often have to construct plans that obey resource limits for continuous resources whose consumption can only be characterized by probability distributions. While Markov Deci...