Answer set programming with resources

In this paper, we propose an extension of Answer Set Programming (ASP) to support declarative reasoning on consumption and production of resources. We call the proposed extension RASP, standing for "Resourced ASP". Resources are modeled by introducing special atoms, called amount-atoms, to which we associate quantities that represent the available amount of a certain resource. The "firing" of a RASP-rule involving amount-atoms can both consume and produce resources. A RASP-rule can be fired several times, according to its definition and to the available quantities of required resources. We define the semantics for RASP programs by extending the usual answer set semantics. Different answer sets correspond to different possible allocations of available resources. We then propose an implementation based on standard ASP-solvers. The implementation consists of a standard translation of each RASP-rule into a set of plain ASP rules and of an inference engine that manages the firing of RASP-rules

BRANCH: an ASP systems benchmark for resource allocation in business processes

The goal of BRANCH is to benchmark Answer Set Programming (ASP) systems to test their performance when dealing with the task of automatically allocating resources to business process activities. Like many other scheduling problems, the allocation of resources and starting times to process activities is a challenging optimization problem, yet it is a crucial step for an optimal execution of the processes. BRANCH has been designed as a configurable benchmark equipped with instance generators that produce problem instances of different size and hardness with respect to adjustable parameters. This application-oriented benchmark supports the BPM community to find the ASP systems and implementations that perform better in solving the resource allocation problem.Ministerio de Ciencia e Innovación RTI2018-100763-J-I0

Entity set expansion from the Web via ASP

Knowledge on the Web in a large part is stored in various semantic resources that formalize, represent and organize it differently. Combining information from several sources can improve results of tasks such as recognizing similarities among objects. In this paper, we propose a logic-based method for the problem of entity set expansion (ESE), i.e. extending a list of named entities given a set of seeds. This problem has relevant applications in the Information Extraction domain, specifically in automatic lexicon generation for dictionary-based annotating tools. Contrary to typical approaches in natural languages processing, based on co-occurrence statistics of words, we determine the common category of the seeds by analyzing the semantic relations of the objects the words represent. To do it, we integrate information from selected Web resources. We introduce a notion of an entity network that uniformly represents the combined knowledge and allow to reason over it. We show how to use the network to disambiguate word senses by relying on a concept of optimal common ancestor and how to discover similarities between two entities. Finally, we show how to expand a set of entities, by using answer set programming with external predicates

Inductive learning of answer set programs for autonomous surgical task planning

The quality of robot-assisted surgery can be improved and the use of hospital resources can be optimized by enhancing autonomy and reliability in the robot’s operation. Logic programming is a good choice for task planning in robot-assisted surgery because it supports reliable reasoning with domain knowledge and increases transparency in the decision making. However, prior knowledge of the task and the domain is typically incomplete, and it often needs to be refined from executions of the surgical task(s) under consideration to avoid sub-optimal performance. In this paper, we investigate the applicability of inductive logic programming for learning previously unknown axioms governing domain dynamics. We do so under answer set semantics for a benchmark surgical training task, the ring transfer. We extend our previous work on learning the immediate preconditions of actions and constraints, to also learn axioms encoding arbitrary temporal delays between atoms that are effects of actions under the event calculus formalism. We propose a systematic approach for learning the specifications of a generic robotic task under the answer set semantics, allowing easy knowledge refinement with iterative learning. In the context of 1000 simulated scenarios, we demonstrate the significant improvement in performance obtained with the learned axioms compared with the hand-written ones; specifically, the learned axioms address some critical issues related to the plan computation time, which is promising for reliable real-time performance during surgery

Argumentation and Defeasible Reasoning in the Law

Different formalisms for defeasible reasoning have been used to represent knowledge and reason in the legal field. In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: defeasible logic, Answer Set Programming, ABA+, ASPIC+, and DeLP. We compare features of these approaches under three perspectives: the logical model (knowledge representation), the method (computational mechanisms), and the technology (available software resources). On top of that, two real examples in the legal domain are designed and implemented in ASPIC+ to showcase the benefit of an argumentation approach in real-world domains. The CrossJustice and Interlex projects are taken as a testbed, and experiments are conducted with the Arg2P technology

Answer Set Programming with External Sources

Answer Set Programming (ASP) is a well-known problem solving approach based on nonmonotonic logic programs and efficient solvers. To enable access to external information, HEX-programs extend programs with external atoms, which allow for a bidirectional communication between the logic program and external sources of computation (e.g., description logic reasoners and Web resources). Current solvers evaluate HEX-programs by a translation to ASP itself, in which values of external atoms are guessed and verified after the ordinary answer set computation. This elegant approach does not scale with the number of external accesses in general, in particular in presence of nondeterminism (which is instrumental for ASP). Hence, there is a need for genuine algorithms which handle external atoms as first-class citizens, which is the main focus of this PhD project. In the first phase of the project, state-of-the-art conflict driven algorithms were already integrated into the prototype system dlvhex and extended to external sources. In particular, the evaluation of external sources may trigger a learning procedure, such that the reasoner gets additional information about the internals of external sources. Moreover, problems on the second level of the polynomial hierarchy were addressed by integrating a minimality check, based on unfounded sets. First experimental results show already clear improvements