Yanıt kümelerinin hesaplanmasında model oluşturabilen teorem ispatlayıcılarının kullanılması.

Answer set programming (ASP) is a declarative approach to solving search problems. Logic programming constitutes the foundation of ASP. ASP is not a proof-theoretical approach where you get solutions by answer substitutions. Instead, the problem is represented by a logic program in such a way that models of the program according to the answer set semantics correspond to solutions of the problem. Answer set solvers (Smodels, Cmodels, Clasp, and Dlv) are used for finding answer sets of a given program. Although users can write programs with variables for convenience, current answer set solvers work on ground logic programs where there are no variables. The grounding step of ASP generates a propositional instance of a logic program with variables. It may generate a huge propositional instance and make the search process of answer set solvers more difficult. Model generation theorem provers (Paradox, Darwin, and FM-Darwin) have the capability of producing a model when the first-order input theory is satisfiable. This work proposes the use of model generation theorem provers as computational engines for ASP. The main motivation is to eliminate the grounding step of ASP completely or to perform it more intelligently using the model generation system. Additionally, regardless of grounding, model generation systems may display better performance than the current solvers. The proposed method can be seen as lifting SAT-based ASP, where SAT solvers are used to compute answer sets, to the first-order level for tight programs. A completion procedure which transforms a logic program to formulas of first-order logic is utilized. Besides completion, other transformations which are necessary for forming a firstorder theory suitable for model generation theorem provers are investigated. A system called Completor is implemented for handling all the necessary transformations. The empirical results demonstrate that the use of Completor and the theorem provers together can be an e ective way of computing answer sets. Especially, the run time results of Paradox in the experiments has showed that using Completor and Paradox together is favorable compared to answer set solvers. This advantage has been more clearly observed for programs with large propositional instances, since grounding can be a bottleneck for such programs.Ph.D. - Doctoral Progra

Computing Answer Sets Using Model Generation Theorem Provers

Model generation theorem provers have the capability of producing a model when the first-order input theory is satisfiable. Because grounding step may generate huge propositional instances of the program it hardens the search process of answer set solvers. We propose the use of model generation theorem provers as computational engines for Answer Set Programming (ASP). It can be seen as lifting of SAT-based ASP to the first-order level for tight programs to eliminate the grounding step of ASP or do it more intelligently

Integrating ASP into ROS for Reasoning in Robots

To appearInternational audienceKnowledge representation and reasoning capacities are vitalto cognitive robotics because they provide higher level functionalities forreasoning about actions, environments, goals, perception, etc. AlthoughAnswer Set Programming (ASP) is well suited for modelling such func-tions, there was so far no seamless way to use ASP in a robotic setting.Weaddress this shortcoming and show how a recently developed ASP sys-tem can be harnessed to provide appropriate reasoning capacities withina robotic system. To be more precise, we furnish a package integratingthe new version of the ASP solver clingo with the popular open-sourcerobotic middleware ROS. The resulting system, ROSoClingo, providesa generic way by which an ASP program can be used to control thebehaviour of a robot and to respond to the results of the robot's actions

Solving Goal Recognition Design Using ASP

Goal Recognition Design involves identifying the best ways to modify an underlying environment that agents operate in, typically by making asubset of feasible actions infeasible, so that agents are forced to reveal their goals as early as possible. Thus far, existing work has focused exclusively on imperative classical planning. In this paper, we address the same problem with a different paradigm, namely, declarative approaches based on Answer Set Programming (ASP). Our experimental results show that one of our ASP encodings is more scalable and is significantly faster by up to three orders of magnitude than thecurrent state of the art