Extending ASP with constraints (CASP) enhances its expressiveness and performance. This extension is not straightforward as the grounding phase, present in most ASP systems, removes variables and the links among them, and also causes a combinatorial explosion in the size of the program. Several methods to overcome this issue have been devised: restricting the constraint domains (e.g., discrete instead of dense), or the type (or number) of models that can be returned. In this paper we propose to incorporate constraints into s(ASP), a goal-directed, top-down execution model which implements ASP while retaining logical variables both during execution and in the answer sets. The resulting model, s(CASP), can constrain variables that, as in CLP, are kept during the execution and in the answer sets. s(CASP) inherits and generalizes the execution model of s(ASP) and is parametric w.r.t. the constraint solver. We describe this novel execution model and show through several examples the enhanced expressiveness of s(CASP) w.r.t. ASP, CLP, and other CASP systems. We also report improved performance w.r.t. other very mature, highly optimized ASP systems in some benchmarks.

Management of chronic diseases such as heart failure, diabetes, and chronic obstructive pulmonary disease (COPD) is a major problem in health care. A standard approach that the medical community has devised to manage widely prevalent chronic diseases such as chronic heart failure (CHF) is to have a committee of experts develop guidelines that all physicians should follow. These guidelines typically consist of a series of complex rules that make recommendations based on a patient's information. Due to their complexity, often the guidelines are either ignored or not complied with at all, which can result in poor medical practices. It is not even clear whether it is humanly possible to follow these guidelines due to their length and complexity. In the case of CHF management, the guidelines run nearly 80 pages. In this paper we describe a physicianadvisory system for CHF management that codes the entire set of clinical practice guidelines for CHF using answer set programming. Our approach is based on developing reasoning templates (that we call knowledge patterns) and using these patterns to systemically code the clinical guidelines for CHF as ASP rules. Use of the knowledge patterns greatly facilitates the development of our system. Given a patient's medical information, our system generates a recommendation for treatment just as a human physician would, using the guidelines. Our system will work even in the presence of incomplete information. Our work makes two contributions: (i) it shows that highly complex guidelines can be successfully coded as ASP rules, and (ii) it develops a series of knowledge patterns that facilitate the coding of knowledge expressed in a natural language and that can be used for other application domains. This paper is under consideration for acceptance in TPLP.

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Abstract. We give an overview of the coinductive logic programming paradigm. We discuss its applications to modeling ω-automata, model checking, verification, non-monotonic reasoning, developing SAT solvers, etc. We also discuss future research directions.

Management of heart failure is a major health care challenge. Healthcare providers are expected to use best practices described in clinical practice guidelines, which typically consist of a long series of complex rules. For heart failure management, the relevant guidelines are nearly 80 pages long. Due to their complexity, the guidelines are often difficult to fully comply with, which can result in suboptimal medical practices. In this paper, we describe a heart failure treatment adviser system that automates the entire set of rules in the guidelines for heart failure management. The system is based on answer set programming, a form of declarative programming suited for simulating human-style reasoning. Given a patient’s information, the system is able to generate a set of guideline-compliant recommendations. We conducted a pilot study of the system on 21 real and 10 simulated patients with heart failure. The results show that the system can give treatment recommendations compliant with the guidelines. Out of 187 total recommendations made by the system, 176 were agreed upon by the expert cardiologists. Also, the system missed eight valid recommendations. The reason for the missed and discordant recommendations seems to be insufficient information, differing style, experience, and knowledge of experts in decision-making that were not captured in the system at this time. The system can serve as a point-of-care tool for clinics. Also, it can be used as an educational tool for training physicians and an assessment tool to measure the quality metrics of heart failure care of an institution.

In answer set programming, inconsistencies arise when the constraints placed on a program become unsatisfiable. In this paper, we introduce a technique for dynamic consistency checking for our goal-directed method for computing answer sets, under which only those constraints deemed relevant to the partial answer set are tested, allowing inconsistent knowledgebases to be successfully queried. However, the algorithm guarantees that, if a program has at least one consistent answer set, any partial answer set returned will be a subset of some consistent answer set. To appear in Theory and Practice of Logic Programming (TPLP).Comment: 12 pages. Accepted to ICLP 2014. To appear in Theory and Practice of Logic Programming (TPLP

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Answer Set Programming (ASP) has emerged as a successful paradigm for developing intelligent applications. ASP is based on adding negation as failure to logic programming under the stable model semantics regime. ASP allows for sophisticated reasoning mechanisms that are employed by humans to be modeled elegantly. We argue that being able to model common sense reasoning as used by humans is critical for success of automated reasoning. We also argue that extending answer programming systems to general predicates is critical to realizing the full power of ASP. Goal-directed predicate ASP systems are needed to make the ASP technology practical for building large, scalable knowledge-based applications.