Dealing Automatically with Exceptions by Introducing Specificity in ASP

Answer Set Programming (ASP), via normal logic programs, is known as a suitable framework for default reasoning since it offers both a valid formal model and operational systems. However, in front of a real world knowledge representation problem, it is not easy to represent information in this framework. That is why the present article proposed to deal with this issue by generating in an automatic way the suitable normal logic program from a compact representation of the information. This is done by using a method, based on specificity, that has been developed for default logic and which is adapted here to ASP both in theoretical and practical points of view

The Livestock Mandatory Reporting Act: Could It Be A Mixed Blessing?

The Livestock Mandatory Reporting Act of 1999 (the Act), which went into effect February 1, 2001, requires meat packers to report detailed price and quantity information on cattle, hogs, lambs and products to the USDA\u27s Agricultural Marketing Service (AMS) on a daily basis. Historically, packers reported to AMS on a voluntary basis. However, “as more animals are being bought and sold under marketing arrangements where neither the arrangements nor the final purchase prices are publicly disclosed, ... it has become more difficult for producers to determine the actual prevailing purchasing price for livestock..” So, by making reports to AMS mandatory rather than voluntary, the Act aims to make livestock markets more transparent, thus “provid[ing] timely, accurate, and reliable market information, facilitat[ing] more informed marketing decisions and promot[ing] competition in the industry.

On the Semantics of Logic Programs with Preferences

This work is a contribution to prioritized reasoning in logic programming in the presence of preference relations involving atoms. The technique, providing a new interpretation for prioritized logic programs, is inspired by the semantics of Prioritized Logic Programming and enriched with the use of structural information of preference of Answer Set Optimization Programming. Specifically, the analysis of the logic program is carried out together with the analysis of preferences in order to determine the choice order and the sets of comparable models. The new semantics is compared with other approaches known in the literature and complexity analysis is also performed, showing that, with respect to other similar approaches previously proposed, the complexity of computing preferred stable models does not increase

Algorithms for Solving Satisfiability Problems with Qualitative Preferences

Abstract. In this work we present a complete picture of our work on computing optimal solutions in satisfiability problems with qualitative preferences. With this task in mind, we first review our work on computing optimal solutions by imposing an ordering on the way the search space is explored, e.g., on the splitting heuristic in case the DPLL algorithm is used. The main feature of this approach is that it guarantees to compute all and only the optimal solutions, i.e., models which are not optimal are not even computed: For this result, it is essential that the splitting heuristic of the solver follows the partial order on the expressed preferences. However, for each optimal solution, a formula that prunes non-optimal solutions needs to be retained, thus this procedure does not work in polynomial space when computing all optimal solutions. We then extend our previous work and show how it is possible to compute optimal solutions using a generate-and-test approach: Such a procedure is based on the idea to first compute a model and then check for its optimality. As a consequence, no ordering on the splitting heuristic is needed, but it may compute also nonoptimal models. This approach does not need to retain formulas indefinitely, thus it does work in polynomial space. We start from a simple setting in which a preference is a partial order on a set of literals. We then show how other forms of preferences, i.e., quantitative, qualitative on formulas and mixed qualitative/quantitative can be captured by our framework, and present alternatives for computing “complete ” sets of optimal solutions. We finally comment on the implementation of the two procedures on top of state-of-the-art satisfiability solvers, and discuss related work.