Finding similar or diverse solutions in answer set programming: theory and applications

For many computational problems, the main concern is to find a best solution (e.g., a most preferred product configuration, a shortest plan, a most parsimonious phylogeny) with respect to some well-described criteria. On the other hand, in many real-world applications, computing a subset of good solutions that are similar/diverse may be desirable for better decision-making. For one reason, the given computational problem may have too many good solutions, and the user may want to examine only a few of them to pick one; in such cases, finding a few similar/diverse good solutions may be useful. Also, in many real-world applications the users usually take into account further criteria that are not included in the formulation of the optimization problem; in such cases, finding a few good solutions that are close to or distant from a particular set of solutions may be useful. With this motivation, we have studied various computational problems related to finding similar/diverse (resp. close/distant) solutions with respect to a given distance function, in the context of Answer Set Programming (ASP). We have introduced novel offline/online computational methods in ASP to solve such computational problems. We have modified an ASP solver according to one of our online methods, providing a useful tool (CLASP-NK) for various ASP applications. We have showed the applicability and effectiveness of our methods/tools in three domains: phylogeny reconstruction, AI planning, and biomedical query answering. Motivated by the promising results, we have developed computational tools to be used by the experts in these areas

Monitoring Agents using Declarative Planning

Abstract. We consider the following problem: Given a particular description of a multi-agent system (MAS), is it implemented properly? We assume we are given (possibly incomplete) information and aim at refuting that the given system is implemented properly. In our approach, agent collaboration is described as an action theory. Action sequences reaching the collaboration goal are computed by a planner, whose compliance with the actual MAS behaviour allows to detect possible collaboration failures. The approach can be fruitfully applied to aid offline testing of a MAS implementation

Monitoring Agents using Declarative Planning ¡

eiter,fink,polleres¦ Abstract. We present an agent monitoring approach, which aims at refuting from (possibly incomplete) information at hand that a multi-agent system (MAS) is implemented properly. In this approach, agent collaboration is abstractly described in an action theory. Action sequences reaching the collaboration goal are determined by a planner, whose compliance with the actual MAS behavior allows to detect possible collaboration failures. The approach can be fruitfully applied to aid offline testing of a MAS implementation, as well as online monitoring