This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make ...
This paper develops a new paradigm for relational learning which allows for the representation and learning of relational information using propositional means. This paradigm sugg...
Groundedmodels(Siena2001b)differ fromaxiomatictheories in establishingexplicit connectionsbetweenlanguage andreality that are learnedthroughlanguagegames(Wittgenstein 1953).Thispa...
Although a partially observable Markov decision process (POMDP) provides an appealing model for problems of planning under uncertainty, exact algorithms for POMDPs are intractable...
In this paper we present the monotonicity principle, a sufficient condition to ensure that exact mapping, a mapping as would be performed by a human observer, is ranked close to ...
Ateret Anaby-Tavor, Avigdor Gal, Alberto Trombetta