As we face the actual possibility of modelling agent systems capable of nondeterministic self-evolution, we are confronted with the problem of having several different possible futures for any single agent. This issue brings the challenge of how to allow such evolving agents to be able to look ahead, prospectively, into such hypothetical futures, in order to determine the best courses of evolution from their own present, and thence to prefer amongst them. The concept of prospective logic programs is presented as a way to address such issues. We start by building on previous theoretical background, on evolving programs and on abduction, to construe a framework for prospection and describe an abstract procedure for its materialization. We take on several examples of modelling prospective logic programs that illustrate the proposed concepts and briefly discuss the ACORDA system, a working implementation of the previously presented procedure. We conclude by elaborating about current limitations of the system and examining future work scenaria.

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

As we face the real possibility of modelling programs that are capable of nondeterministic self-evolution, we are confronted with the problem of having several different possible futures for a single such program. It is desirable that such a system be somehow able to look ahead, prospectively, into such possible futures, in order to determine the best courses of evolution from its own present, and then to prefer amongst them. This is the objective of the ACORDA, a prospective logic programming system. We start from a real-life working example of differential medical diagnosis illustrating the benefits of addressing these concerns, and follow with a brief description of the concepts and research results supporting ACORDA, and on to their implementation. Then we proceed to fully specify the implemented system and how we addressed each of the enounced challenges. Next, we take on the proffered example, as codified into the system, and describe the behaviour of ACORDA as we carefully detail the resulting steps involved. Finally, we elaborate upon several considerations regarding the current limitations of the system, and conclude with the examination of possibilities for future work. 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.

For practical applications, the use of top-down query-driven proofprocedures is essential for an efficient use and computation of answers using Logic Programs as knowledge bases. Additionally, abductive reasoning on demand is intrinsically a top-down search method. A query-solving engine is thus highly desirable. The current standard 2-valued semantics for Normal Logic Programs (NLPs), the Stable Models (SMs) semantics, does not allow for top-down query-solving because it does not enjoy the relevance property — and moreover, it does not guarantee the existence of a model for every NLP. To overcome these current limitations we introduce here a new 2-valued semantics for NLPs — the Layered Models semantics — which conservatively extends the SMs, enjoys relevance and guarantees model existence among other useful properties. Moreover, for existential query answering there is no need to compute total models, but just the partial models that sustain the answer to the query, or one might simply know a model one exists without producing it; relevance ensures these can be extended to total models. A first implementation of a query-solving engine based on this new semantics is presented and described here. It uses the XSB-Prolog engine and its XASP interface to Smodels, thereby providing a useful tool built as a hybrid of the two systems and taking advantage of the best of each. Conclusions and further work end the paper.

Abstract. We present a system (ASP − PROLOG) which provides a tight and well-defined integration of Prolog and Answer Set Programming (ASP). The combined system enhances the expressive power of ASP, allowing us to write programs that reason about dynamic ASP modules and about collections of stable models. These features are vital in a number of application domains (e.g., planning, scheduling, diagnosis). We describe the design of ASP − PROLOG along with its implementation, realized using CIAO Prolog and Smodels. 1

Abstract. In this paper we develop a declarative language for modular answer set programming (ASP). Our language allows to declaratively state how one ASP module can import processed answer sets from another ASP module. We define the syntax and semantics of our language and illustrate its applicability by modeling the ACC tournament scheduling problem. Besides the elegance of developing declarative programs in a modular manner, our illustration shows that a problem that is not timely solvable when done in a monolithic way, but becomes solvable when done in a modular way. 1

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.

Abstract. This work proposes the application of preferences over abductive logic programs as an appealing declarative formalism to model choice situations. In particular, both a priori and a posteriori handling of preferences between abductive extensions of a theory are addressed as complementary and essential mechanisms in a broader framework for abductive reasoning. Furthermore, both of these choice mechanisms are combined with other formalisms for decision making, like economic decision theory, resulting in theories containing the best advantages from both qualitative and quantitative formalisms. Several examples are presented throughout to illustrate the enounced methodologies. These have been tested in our implementation, which we explain in detail. Key words. Abduction, Preferences, Logic Programming, XSB-Prolog, Smodels 1