We present a new algorithm for probabilistic planning with no observability. Our algorithm, called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-F...
The aim of this paper is to extend the modal logic of knowledge due to Moss and Parikh by state transformers arising, eg, from actions of agents. The peculiarity of Moss and Parik...
Imitation represents a powerful approach for programming and autonomous learning in robot and computer systems. An important aspect of imitation is the mapping of observations to ...
Current rule base maintenance is wasting refinement and inference performance. There are only few maintenance concepts, which enjoy both (1) formal rule refinement and (2) utili...
This paper develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans. PHA...