Answer Set Programming Approach Research Articles

Commonsense reasoning and automatic generation of IoT contextual knowledge: An Answer Set Programming approach

The increasing interest in searching for intelligent, proactive, and autonomous environments leads to the necessity of accessing contextual knowledge: knowledge about changes that occur in the environment and even about the capabilities of the devices that compose a specific deployment. This knowledge would allow carrying out commonsense reasoning (exhibit human-like comprehension) according to the current situation, enabling decision making and task planning. One of the most used forms of knowledge specification is ontologies, but ontological knowledge modeling is usually a manual process, making it a costly task. In addition, ontologies can be found that have been designed for specifying knowledge about IoT-related concepts, but many are overly complex or do not have an adequate orientation that allows modeling the system’s capabilities. There are many different types of reasoners, but Answer Set Solvers are of particular interest for commonsense reasoning. This paper proposes an ontology to represent contextual knowledge about IoT device capabilities, and an Answer Set Programming-based reasoner for the automatic generation of this knowledge. The main challenge is to demonstrate that contextual knowledge can be generated through commonsense reasoning processes implemented with Answer Set Programming, and that the resulting knowledge can be used for decision making (also through commonsense reasoning). This work shows how the generated knowledge is correct through different use cases, presents an application example that demonstrates the benefits of using the generated knowledge, and analyzes the reasoner performance to demonstrate that the execution time is adequate.

Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach

AbstractThis paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.

Decomposition approaches for scheduling chronic outpatients’ clinical pathways in Answer Set Programming

Abstract Chronic patients suffering from non-communicable diseases are often enrolled into a diagnostic and therapeutic care program featuring a personalized care plan. Healthcare is mostly provided at the patient’s home, but those examinations and treatments that must be delivered at the hospital have to be explicitly booked. Booking is not trivial due to, on the one hand, the several time constraints that become particularly tight in the case of comorbidity, on the other hand, the limited availability of both staff and equipment at the hospital care units. This suggests that the scheduling of the clinical pathways for enrolled outpatients should be managed in a centralized manner, taking advantage of the fact that demand for services is known well in advance. The aim is to serve as many requests as possible (unattended requests are supplied by contracted private health facilities) in a timely manner, taking patients priority into account. Booking involves setting a date and a time for each selected health service, which is rather complex. In this work, we provide a declarative approach by encoding the problem in Answer Set Programming (ASP). In order to improve the scalability of the ASP approach, we present and compare two heuristic approaches, respectively based on service demand and time decomposition. All approaches are tested on instances of increasing size to assess scalability with respect to time horizon and number of requests.

Hybrid planning for challenging construction problems: An Answer Set Programming approach

We study construction problems where multiple robots rearrange stacks of prefabricated blocks to build stable structures. These problems are challenging due to ramifications of actions, true concurrency, and requirements of supportedness of blocks by a surface or a robot and stability of the overall structure at all times. We propose a general elaboration tolerant method to solve a wide range of construction problems, based on the knowledge representation and reasoning paradigm of Answer Set Programming. This method not only (i) determines a stable final configuration of the structure, but also (ii) computes the order of manipulation tasks for multiple autonomous robots to build the structure from an initial configuration, (iii) while simultaneously ensuring the requirements of supportedness and stability at all times. We prove the soundness and completeness of our method with respect to these properties. We introduce a set of challenging construction benchmark instances, including construction of (uneven) bridges and overhangs, and discuss the usefulness of our framework over these instances. Furthermore, we perform experiments to investigate the computational performance of our hybrid method, and demonstrate the applicability of our method using a bimanual Baxter robot.

Declarative and Mathematical Programming approaches to Decision Support Systems for food recycling

Every year about one third of the food production intended for humans gets lost or wasted. This wastefulness of resources leads to the emission of unnecessary greenhouse gas, contributing to global warming and climate change.The solution proposed by the SORT project is to “recycle” the surplus of food by reconditioning it into animal feed or fuel for biogas/biomass power plants. In order to maximize the earnings and minimize the costs, several choices must be made during the reconditioning process. Given the extremely complex nature of the process, Decision Support Systems (DSSs) could be helpful to reduce the human effort in decision making.In this paper, we present a DSS for food recycling developed using two approaches for finding the optimal solution: one based on Binary Linear Programming (BLP) and the other based on Answer Set Programming (ASP), which outperform our previous approach based on Constraint Logic Programming (CLP) on Finite Domains (CLP(FD)).In particular, the BLP and the CLP(FD) approaches are developed in ECLiPSe, a Prolog system that interfaces with various state-of-the-art Mathematical and Constraint Programming solvers. The ASP approach, instead, is developed in clingo. The three approaches are compared on several synthetic datasets that simulate the operative conditions of the DSS.

Hybrid metabolic network completion

AbstractMetabolic networks play a crucial role in biology since they capture all chemical reactions in an organism. While there are networks of high quality for many model organisms, networks for less studied organisms are often of poor quality and suffer from incompleteness. To this end, we introduced in previous work an answer set programming (ASP)-based approach to metabolic network completion. Although this qualitative approach allows for restoring moderately degraded networks, it fails to restore highly degraded ones. This is because it ignores quantitative constraints capturing reaction rates. To address this problem, we propose a hybrid approach to metabolic network completion that integrates our qualitative ASP approach with quantitative means for capturing reaction rates. We begin by formally reconciling existing stoichiometric and topological approaches to network completion in a unified formalism. With it, we develop a hybrid ASP encoding and rely upon the theory reasoning capacities of the ASP system clingo for solving the resulting logic program with linear constraints over reals. We empirically evaluate our approach by means of the metabolic network of Escherichia coli. Our analysis shows that our novel approach yields greatly superior results than obtainable from purely qualitative or quantitative approaches.

Temporal Conformance Analysis and Explanation of Clinical Guidelines Execution: An Answer Set Programming Approach

Clinical Guidelines (CGs) provide general evidence-based recommendations and physicians often have to resort also to their Basic Medical Knowledge (BMK) to cope with specific patients. In this paper, we explore the interplay between CGs and BMK from the viewpoint of a-posteriori conformance analysis, intended as the adherence of a specific execution log to both the CG and the BMK. In this paper, we consider also the temporal dimension: the guideline may include temporal constraints for the execution of actions, and its adaptation to a specific patient and context may add or modify conditions and temporal constraints for actions. We propose an approach for analyzing execution traces in Answer Set Programming with respect to a guideline and BMK, pointing out discrepancies – including temporal discrepancies – with respect to the different knowledge sources, and providing explanations regarding how the applications of the CG and the BMK have interacted, especially in case strictly adhering to both is not possible.

0-1 Integer Programming for Computing Semi-Stable Semantics of Argumentation Frameworks

Dung's abstract argumentation has been object of intense study not just for its relationship with logical reasoning but also for its uses within artificial intelligence. One research branch in abstract argumentation has focused on finding new methods for computing its different semantics. We present a novel method, to the best of our knowledge, for computing semi-stable semantics using 0-1 integer programming. This approach captures the notions of conflict freeness, acceptability, maximality with regard to set inclusion, etc., by 0-1 integer constraints. Additionally, this work also presents an empirical experiment to compare this novel approach with an answer set programming approach. Our results indicate that this new method performed well, and it has a great opportunity space for improving.

Logic programming approaches for routing fault-free and maximally parallel wavelength-routed optical networks-on-chip (Application paper)

AbstractOne promising trend in digital system integration consists of boosting on-chip communication performance by means of silicon photonics, thus materializing the so-called Optical Networks-on-Chip. Among them, wavelength routing can be used to route a signal to destination by univocally associating a routing path to the wavelength of the optical carrier. Such wavelengths should be chosen so to minimize interferences among optical channels and to avoid routing faults. As a result, physical parameter selection of such networks requires the solution of complex constrained optimization problems. In previous work, published in the proceedings of the International Conference on Computer-Aided Design, we proposed and solved the problem of computing the maximum parallelism obtainable in the communication between any two endpoints while avoiding misrouting of optical signals. The underlying technology, only quickly mentioned in that paper, is Answer Set Programming. In this work, we detail the Answer Set Programming approach we used to solve such problem.Another important design issue is to select the wavelengths of optical carriers such that they are spread across the available spectrum, in order to reduce the likelihood that, due to imperfections in the manufacturing process, unintended routing faults arise. We show how to address such problem in Constraint Logic Programming on Finite Domains.

Reasoning About Policies in Security-Aware Service Discovery Using Answer Set Programming

In order to enable a secure interaction between dynamically discovered software services and the client’s application in a cooperative information system such as service oriented system, one of the pre-requisites is the reconciliation of service-specific security policies of all stakeholders. Existing service discovery research does not address the issue of enormous search space in finding security-aware services based on preferred security policy alternatives of the client of software services. In this paper, we propose an answer set programming (ASP) approach, drawn from the field of artificial intelligence (AI), to explore a viable solution of finding security-aware services for the client. We argue that the ASP approach can significantly reduce the search space and achieve great performance gains. We use ASP to: (i) specify security policies including expressing service-specific security preference weighting and importance scoring in quantifiable terms; and (ii) reason about the compliance between the security policies of the client and the software service.

Semantic-based construction of arguments: An answer set programming approach

In this paper, we introduce an argumentation approach which takes an extended logic program as input and gives a set of arguments with the respective disagreements among them as output. We establish the notion of an argument under the Well-Founded semantics and Stable semantics inferences, allowing us to identify arguments with stratified programs as support, even when the input for the argument engine is a non-stratified program. We propose a set of rationality postulates for argument-based systems under extended logic programs, which are based on a definition of closure for a set of clauses that considers the well-known Gelfond–Lifschitz reduction. We establish the conditions under which our approach satisfies these principles. In addition, we present a standalone argumentation-tool based on the XSB system which implements our argumentation approach.

An answer set programming encoding of Prioritized Removed Sets Revision: application to GIS

Geographical information systems are ones of the most important application areas of belief revision. Recently, Wurbel and colleagues (Proceedings of the seventh international conference about principles of knowledge representation and reasoning, KR2000, pp. 505---516, 2000) have applied the so-called removed sets (RSR) to the problem of assessment of water heights in a flooded valley. The application was partially satisfactory since only a small part of the valley has been handled. This paper goes one step further, and proposes an extension of (RSR) called Prioritized Removed Sets Revision (PRSR). We show that (PRSR) performed using answer set programming makes possible to solve a practical revision problem provided by a real application in the framework of geographical information system (GIS). We first show how PRSR can be encoded into a logic program with answer set semantics, we then present an adaptation of the smodels system devoted to efficiently compute the answer sets in order to perform PRSR. The experimental study shows that the answer set programming approach gives better results than previous implementations of RSR and in particular it allows to handle the whole valley. Lastly, some experimental studies comparing our encoding with implementations based on SAT-solvers are also provided.