Researchers in answer set programming and constraint programming have 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. Constraint answer set programming languages and systems proved to be successful at providing declarative, yet efficient solutions to problems involving hybrid reasoning tasks. One of the main contributions of this paper is the first comprehensive account of the constraint answer set language and solver ezcsp, a mainstream representative of this research area that has been used in various successful applications. We also develop an extension of the transition systems proposed by Nieuwenhuis et al. in 2006 to capture Boolean satisfiability solvers. We use this extension to describe the ezcsp algorithm and prove formal claims about it. The design and algorithmic details behind ezcsp clearly demonstrate that the development of the hybrid systems of this kind is challenging. Many questions arise when one faces various design choices in an attempt to maximize system's benefits. One of the key decisions that a developer of a hybrid solver makes is settling on a particular integration schema within its implementation. Thus, another important contribution of this paper is a thorough case study based on ezcsp, focused on the various integration schemas that it provides.

In spite of the improvements in the performance of many solvers for model-based languages, it is still possible for the search algorithm to focus on the wrong areas of the search space, preventing the solver from returning a solution in an acceptable

Dealing with domains involving substantial quantitative information in Answer Set Programming (ASP) often results in cumbersome and inefficient encodings. Hybrid “CASP” languages combining ASP and Constraint Programming aim to overcome this limitation, but also impose inconvenient constraints – first and foremost that quantitative information must be encoded by means of total functions. This goes against central knowledge representation principles that contribute to the power of ASP, and makes the formalization of certain domains difficult. ASP{f} is being developed with the ultimate goal of providing scientists and practitioners with an alternative to CASP languages that allows for the efficient representation of qualitative and quantitative information in ASP without restricting one's ability to deal with incompleteness or uncertainty. In this paper we present the latest outcome of such research: versions of the language and of the supporting system that allow for practical, industrial-size use and scalability. The applicability of ASP{f} is demonstrated by a case study on an actual industrial application.

Constraint answer set programming (CASP) is an extension of answer set programming that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics. In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of theezcspCASP solver in order to solve CASP programs arising from PDDL+ domains. An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of theezcspsolver, other CASP solvers, and PDDL+ planners, shows the viability of our solution.

In this paper we explore the use of Answer Set Programming (ASP) to formalize, and reason about, psychological knowledge. In the field of psychology, a considerable amount of knowledge is still expressed using only natural language. This lack of a formalization complicates accurate studies, comparisons, and verification of theories. We believe that ASP, a knowledge representation formalism allowing for concise and simple representation of defaults, uncertainty, and evolving domains, can be used successfully for the formalization of psychological knowledge. To demonstrate the viability of ASP for this task, in this paper we develop an ASP-based formalization of the mechanics of Short-Term Memory. We also show that our approach can have rather immediate practical uses by demonstrating an application of our formalization to the task of predicting a user's interaction with a graphical interface.