Abstract. The Independent Choice Logic began in the early 90’s as a way to combine logic programming and probability into a coherent framework. The idea of the Independent Choice Logic is straightforward: there is a set of independent choices with a probability distribution over each choice, and a logic program that gives the consequences of the choices. There is a measure over possible worlds that is defined by the probabilities of the independent choices, and what is true in each possible world is given by choices made in that world and the logic program. ICL is interesting because it is a simple, natural and expressive representation of rich probabilistic models. This paper gives an overview of the work done over the last decade and half, and points towards the considerable work ahead, particularly in the areas of lifted inference and the problems of existence and identity. 1

Managing uncertainty and/or vagueness is starting to play an important role in Semantic Web representation languages. Our aim is to overview basic concepts on representing uncertain and vague knowledge in current Semantic Web ontology and rule languages (and their combination).

Abstract. We propose a new approach for implementing P-log using XASP, the interface of XSB with Smodels. By using the tabling mechanism of XSB, our system is most of the times faster than P-log. In addition, our implementation has query features not supported by P-log, as well as new set operations for domain definition. 1

Abstract. In this paper, we describe a novel approach to tackle intention recognition, by combining dynamically configurable and situation-sensitive Causal Bayes Networks plus plan generation techniques. Given some situation, such networks enable recognizing agent to come up with the most likely intentions of the intending agent, i.e. solve one main issue of intention recognition; and, in case of having to make a quick decision, focus on the most important ones. Furthermore, the combination with plan generation provides a significant method to guide the recognition process with respect to hidden actions and unobservable effects, in order to confirm or disconfirm likely intentions. The absence of this articulation is a main drawback of the approaches using Bayes Networks solely, due to the combinatorial problem they encounter.

Abstract. We describe a novel approach to tackle intention recognition, by combining dynamically configurable and situation-sensitive Causal Bayes Networks plus plan generation techniques. Given some situation, such networks enable the recognizing agent to come up with the most likely intentions of the intending agent, i.e. solve one main issue of intention recognition. And, in case of having to make a quick decision, focus on the most important ones. Furthermore, the combination with plan generation provides a significant method to guide the recognition process with respect to hidden actions and unobservable effects, in order to confirm or disconfirm likely intentions. The absence of this articulation is a main drawback of the approaches using Bayes Networks solely, due to the combinatorial problem they encounter. We explore and exemplify its application, in the Elder Care context, of the ability to perform Intention Recognition and of wielding Evolution Prospection methods to help the Elder achieve its intentions. This is achieved by means of an articulate use of a Causal Bayes Network to heuristically gauge probable general intention – combined with specific generation of plans involving preferences – for checking which such intentions are plausibly being carried out in the specific situation at hand, and suggesting actions to the Elder. The overall approach is formulated within one coherent and general logic programming framework and implemented system. The paper recaps required background and illustrates the approach via an extended application example.

Abstract CR-Prolog is an extension of the knowledge representation language A-Prolog. The extension is built around the introduction of consistency-restoring rules (cr-rules for short), and allows an elegant formalization of events or exceptions that are unlikely, unusual, or undesired. The flexibility of the language has been extensively demonstrated in the literature, with examples that include planning and diagnostic reasoning. In this paper we present the design of an inference engine for CR-Prolog that is efficient enough to allow the practical use of the language for medium-size applications. The capabilities of the inference engine have been successfully demonstrated with experiments on an application independently developed for use by NASA. 1

Abstract. We review a logic-based modeling language PRISM and report recent developments including belief propagation by the generalized inside-outside algorithm and generative modeling with constraints. The former implies PRISM subsumes belief propagation at the algorithmic level. We also compare the performance of PRISM with state-of-theart systems in statistical natural language processing and probabilistic inference in Bayesian networks respectively, and show that PRISM is reasonably competitive. 1

This paper describes a family of knowledge representation problems, whose intuitive solutions require reasoning about defaults, the effects of actions, and quantitative probabilities. We describe an extension of the probabilistic logic language P-log (Baral & Gelfond & Rushton 2004), which uses “consistency restoring rules ” to tackle the problems described. We also report the results of a preliminary investigation into the efficiency of our P-log implementation, as compared with ACE(Chavira & Darwiche & Jaeger 2004), a system developed by Automated Reasoning Group at UCLA.

Abstract. We explore and exemplify the application in the Elder Care context of the ability to perform Intention Recognition and of wielding Evolution Prospection methods. This is achieved by means of an articulate use of Causal Bayes Nets (for heuristically gauging probable general intentions), combined with specific generation of plans involving preferences (for checking which such intentions are plausibly being carried out in the specific situation at hand). The overall approach is formulated within one coherent and general logic programming framework and implemented system. The paper recaps required background and illustrates the approach via an extended application example.

This paper provides a study of probabilistic modelling, inference and learning in a logic-based setting. We show how probability densities, being functions, can be represented and reasoned with naturally and directly in higher-order logic, an expressive formalism not unlike the (informal) everyday language of mathematics. We give efficient inference algorithms and illustrate the general approach with a diverse collection of applications. Some learning issues are also considered.