This paper reconsiders the notions of actual cause and explanation in functional causal models. We demonstrate that isomorphic causal models can generate intuitively different cau...
Evolutionary computation is a useful technique for learning behaviors in multiagent systems. Among the several types of evolutionary computation, one natural and popular method is...
This paper presents a novel, promising approach that allows greedy decision tree induction algorithms to handle problematic functions such as parity functions. Lookahead is the st...
In this paper, we evaluate the use of implicit interest indicators as the basis for user profiling in the Digital TV domain. Research in more traditional domains, such as Web brow...
We present an approach for learning part-of-speech distinctions by induction over the lexicon of the Cyc knowledge base. This produces good results (74.6%) using a decision tree t...
A new approach to the Text Categorization problem is here presented. It is called Gaussian Weighting and it is a supervised learning algorithm that, during the training phase, est...
This article describes preliminary work on a research environment called Virtual Synergy to represent a shared virtual map of an area for multiple autonomous robots by modifying t...
Motivated by the matchmaking problem in electronic marketplaces, we study abduction in Description Logics. We devise suitable definitions of the problem, and show how they can mod...
Tommaso Di Noia, Eugenio Di Sciascio, Francesco M....
This paper addresses agents' intentions as building blocks of imitation learning that abstract local situations of the agent, and proposes a hierarchical hidden Markov model ...