A Hybrid Approach to Optimization in Answer Set Programming

Peer reviewe

Delayed predicate algorithm evaluation in hybrid answer set programming (ASP)

A hybrid answer set programming (ASP) solver is a logic programming type solver with a variety of applications, e.g., diagnosing software failures, modeling dynamical systems, etc. A hybrid ASP solver finds solutions for hybrid ASP programs, which are sets of hybrid ASP rules. Hybrid ASP rules include stationary rules with associated predicate algorithms and advancing rules with associated advancing algorithms. Solutions to hybrid ASP programs are found iteratively by starting with an initial state and producing consequent states. Part of computing a consequent state involves determination of the stationary rules that are applicable at the consequent state. This in turn involves evaluating predicate algorithms associated with the rules. This approach is inefficient, since it requires that all the predicate algorithms be evaluated in all the states. The techniques of this disclosure avoid the inefficiency of evaluating predicate algorithms by adding auxiliary rules that enable intelligent guesses as to whether an algorithm will accept or reject, in turn enabling the continued evaluation of the rules without the evaluation of the algorithms

Hybrid conditional planning using answer set programming

We introduce a parallel offline algorithm for computing hybrid conditional plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies on modeling actuation actions and sensing actions in an expressive nonmonotonic language of answer set programming (ASP), and computation of the branches of a conditional plan in parallel using an ASP solver. In particular, thanks to external atoms, continuous feasibility checks (like collision checks) are embedded into formal representations of actuation actions and sensing actions in ASP; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing nonmonotonic constructs and nondeterministic choices, partial knowledge about states and nondeterministic effects of sensing actions can be explicitly formalized in ASP; and thus each branch of a conditional plan can be computed by an ASP solver without necessitating a conformant planner and an ordering of sensing actions in advance. We apply our method in a service robotics domain and report experimental evaluations. Furthermore, we present performance comparisons with other compilation based conditional planners on standardized benchmark domains

Integration Schemas for Constraint Answer Set Programming: a Case Study

Recently, researchers in answer set programming and constraint programming spent significant efforts in the development of hybrid languages and solving algorithms combining the strengths of these traditionally separate fields. These efforts resulted in a new research area: constraint answer set programming (CASP). CASP languages and systems proved to be largely successful at providing efficient solutions to problems involving hybrid reasoning tasks, such as scheduling problems with elements of planning. Yet, the development of CASP systems is difficult, requiring non-trivial expertise in multiple areas. This suggests a need for a study identifying general development principles of hybrid systems. Once these principles and their implications are well understood, the development of hybrid languages and systems may become a wellestablished and well-understood routine process. As a step in this direction, in this paper we conduct a case study aimed at evaluating various integration schemas of CASP methods