taasp 2016

Abstract: Disjunctive Answer Set Programming (ASP) is a powerful declarative programming paradigm whose main decision problems are located on the second level of the polynomial hierarchy. Identifying tractable fragments and developing efficient algorithms for such fragments are thus important objectives in order to complement the sophisticated ASP systems available to date. Hard problems can become tractable if some problem parameter is bounded by a fixed constant; such problems are then called fixed-parameter tractable (FPT). While several FPT results for ASP exist, parameters that relate to directed or signed graphs representing the program at hand have been neglected so far. In this paper, we first give some negative observations showing that directed width measures on the dependency graph of a program do not lead to FPT results. We then consider the graph parameter of signed clique-width and present a novel dynamic programming algorithm that is FPT w.r.t. this parameter. Clique-width is more general than the well-known treewidth, and, to the best of our knowledge, ours is the first FPT algorithm for bounded clique-width for reasoning problems beyond SAT.

Abstract: The CDCL algorithm is the leading solution adopted by state-of-the-art solvers for SAT, SMT, ASP, and others. Experiments show that the performance of CDCL solvers can be significantly boosted by embedding domain-specific heuristics, especially on large real-world problems. However, a proper integration of such criteria in off-the-shelf CDCL implementations is not obvious. In this paper, we distill the key ingredients that drive the search of CDCL solvers, and propose a general framework for designing and implementing new heuristics. We implemented our strategy in an ASP solver, and we experimented on two industrial domains. On hard problem instances, state-of-the-art implementations fail to find any solution in acceptable time, whereas our implementation is very successful and finds all solutions.

Abstract: Answer Set Programming (ASP) is a expressive knowledge representation and reasoning framework. Due to its rather simple syntax paired with high-performance solvers, ASP is interesting for industrial applications. However, errors are human and thus debugging is an important activity during the development process. Therefore, tools for debugging non-ground answer set programs are needed. In this paper, we present a new graphical debugging interface for non-ground answer set programs. The tool is based on the recently-introduced dwasp approach for debugging and simplifies the interaction with the debugger. Furthermore, the debugging interface is integrated in aspide, a rich IDE for answer set programs. With our extension aspide turns into a full-fledged IDE by offering debugging support.

Abstract: When a processing unit relies on data from external streams, we may face the problem that the stream data needs to be rearranged in a way that allows the unit to perform its task(s). On arrival of new data, we must decide whether there is sufficient information available to start processing or whether to wait for more data. Furthermore, we need to ensure that the data meets the input specification of the processing step. In the case of multiple input streams it is also necessary to coordinate which data from which incoming stream should form the input of the next process instantiation. In this work, we propose a declarative approach as an interface between multiple streams and a processing unit. The idea is to specify via answer-set programming how to arrange incoming data in packages that are suitable as input for subsequent processing. Our approach is intended for use in asynchronous multi-context systems (aMCSs), a recently proposed framework for loose coupling of knowledge representation formalisms that allows for online reasoning in a dynamic environment. Contexts in aMCSs process data streams from external sources and other contexts.

Abstract: While the solution counting problem for propositional satisfiability (#SAT) has received renewed attention in recent years, this research trend has not affected other AI solving paradigms like answer set programming (ASP). Although ASP solvers are designed to enumerate all solutions, and counting can therefore be easily done, the involved materialization of all solutions is a clear bottleneck for the counting problem of ASP (#ASP). In this paper we propose dynamic programming-based #ASP algorithms that exploit the structure of the underlying (ground) ASP program. Experimental results for a prototype implementation show promise when compared to existing solvers.

Abstract: Answer Set Programming (ASP) under the stable model semantics has evolved to an extremely powerful approach for representing and solving also industrial-sized combinatorial problems in real-life application domains. ASP supports various language constructs which can be used to express the same realities in syntactically different, but semantically equivalent ways. However, these equivalent programs may not perform equally well. This is because performance depends on the underlying solver implementations which may treat different language constructs differently. As performance is very important for the successful application of ASP in real-life domains, knowledge about the mutual interchangeability and performance of ASP language constructs is crucial for knowledge engineers. In this article, we present an investigation on how the usage of different language constructs affects the performance of state-of-the-art solvers and grounders on the benchmark problems of the ASP competition.

Abstract: Optimization-minimization or maximization-in the lattice of subsets is a frequent operation in Artificial Intelligence tasks. Examples are subset-minimal model-based diagnosis, nonmonotonic reasoning by means of circumscription, or preferred extensions in abstract argumentation. Finding the optimum among many admissible solutions is often harder than finding admissible solutions with respect to both computational complexity and methodology. This paper addresses the former issue by means of an effective method for finding subset-optimal solutions. It is based on the relationship between cardinality-optimal and subset-optimal solutions, and the fact that many logic-based declarative programming systems provide constructs for finding cardinality-optimal solutions, for example maximum satisfiability (MaxSAT) or weak constraints in Answer Set Programming (ASP). Clearly each cardinality-optimal solution is also a subset-optimal one, and if the language also allows for the addition of particular restricting constructs (both MaxSAT and ASP do) then all subset-optimal solutions can be found by an iterative computation of cardinality-optimal solutions. As a showcase, the computation of preferred extensions of abstract argumentation frameworks using the proposed method is studied.

Abstract: Logic-based representations of multi-agent systems have been extensively studied. In this work, we focus on the action language BC to formalize global views of MAS domains. Methodologically, we start representing the behaviour of each agent by an action description from a single agent perspective. Then, it goes through two stages that guide the modeler in composing the global view by first designating multi-agent aspects of the domain via potential conflicts and later resolving these conflicts according to the expected behaviour of the overall system. Considering that representing single agent descriptions is relatively simpler than representing multi-agent description directly, the formalization developed here is valuable from a knowledge representation perspective.