Legal Reasoning in Answer Set Programming

Answer Set Programming is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning. In this article, we provide a logic program, in the context of Answer Set Programming framework, that implements the AGM belief revision process, constructed by means of faithful preorders. The above-mentioned approach constitutes a representative implementation of the Answer Set Programming's modeling methodology, as well as a practical method/construction, bringing us a step closer to the successful development of an AGM belief revision system, for real-world applications.

This symposium was planned to introduce research activities on AI and Law in Japan. It includes five papers. Research into AI and Law in Japan began in 1980, when Hajime Yoshino (Meiji Gakuin University) started the A-project. Since then, he has recruited several lawyers and computer scientists, and developed some legal reasoning systems which handle the civil code. His research activities gained a high evaluation rating, with the result that the Ministry of Education and Science decided to support his ‘Legal Expert’ project for five years (1993–1998). Currently, about 30 lawyers and 10 computer scientists have joined the project and have developed legal reasoning systems. The main research objective has been to develop logic-based legal knowledge bases. Yoshino selected the United Nations Convention for International Sales of Goods (CISG) as the target domain, to maximise the possibilities for international collaboration. Further research arose from the Fifth Generation Computer Systems Project (1982–1995), which developed a wide range of logic-based parallel processing technologies from hardware to application software. While working for the FGCS project, Katsuma Nitta (Electrotechnical Laboratory and Tokyo Institute of Technology) identified legal reasoning systems as a promising application domain. He organized a Legal Reasoning Research Group consisting of eight researchers, and developed legal reasoning systems. Since the FGCS project ended in 1995, Nitta has been continuing the development of these systems in the Electrotechnical Laboratory and Tokyo Institute of Technology. The papers of this collection were selected from the Legal Reasoning System Workshop held during the International Symposium of FGCS 1994, and revised by appending recent research results. All papers are research results of Yoshino’s ‘Legal Expert’ project or of the FGCS project. Yoshino introduces a logic-based knowledge representation language – Compound Predicate Formula (CPF). He investigated the necessary functions of legal knowledge representation languages and formulated a CPF which has been used in legal reasoning systems. The CPF is a conservative extension of the standard 1st

Answer Set Programming (ASP) is a framework in artificial intelligence and knowledge representation for declarative modeling and problem solving. Modern ASP solvers focus on the computation or enumeration of answer sets. However, a variety of probabilistic applications in reasoning or logic programming require counting answer sets. While counting can be done by enumeration, simple enumeration becomes immediately infeasible if the number of solutions is high. On the other hand, approaches to exact counting are of high worst-case complexity. In fact, in propositional model counting, exact counting becomes impractical. In this work, we present a scalable approach to approximate counting for answer set programming. Our approach is based on systematically adding XOR constraints to ASP programs, which divide the search space. We prove that adding random XOR constraints partitions the answer sets of an ASP program. In practice, we use a Gaussian elimination-based approach by lifting ideas from SAT to ASP and integrating it into a state of the art ASP solver, which we call ApproxASP. Finally, our experimental evaluation shows the scalability of our approach over the existing ASP systems.

Answer Set Programming (ASP) is a well established logical approach in artificial intelligence that is widely used for knowledge representation and problem solving. Equilibrium logic extends answer set semantics to more general classes of programs and theories. When intertheory relations are studied in ASP, or in the more general form of equilibrium logic, they are usually understood in the form of comparisons of the answer sets or equilibrium models of theories or programs. This is the case for strong and uniform equivalence and their relativised and projective versions. However, there are many potential areas of application of ASP for which query answering is relevant and a comparison of programs in terms of what can be inferred from them may be important. We formulate and study some natural equivalence and entailment concepts for programs and theories that are couched in terms of inference and query answering. We show that, for the most part, these new intertheory relations coincide with their model-theoretic counterparts. We also extend some previous results on projective entailment for theories and for the new connective called fork.

Answer Set Programming (ASP) has emerged in the recent years as a powerful paradigm for declarative problem solving, which has its roots in knowledge representation and non-monotonic logic programming. Similar to SAT solving, the basic idea is to encode solutions to a problem in the models of a non-monotonic logic program, which can be computed by reasoning engines off the shelf. ASP is particularly well-suited for modeling and solving problems which involve common sense reasoning or transitive closure, and has been fruitfully applied to a growing range of applications. Among the latter are also problems in testing and verfication, for which efficient core fragments of ASP that embrace Datalog haven been exploited. This talk gives a brief introduction to ASP, covering the basic concepts, some of its properties and features, and solvers. It further addresses some applications in the context of verification and recent developments in ASP, which bring evaluation closer to other formalisms and logics.

An investigation in parallel execution of answer set programs on distributed memory platforms: Task sharing and dynamic scheduling

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.

Answer Set Programming (ASP) is a powerful declarative programming paradigm commonly used for solving challenging search and optimization problems. The modeling languages of ASP are supported by sophisticated solving algorithms (solvers) that make the solution search efficient while enabling the programmer to model the problem at a high level of abstraction. As an approach to Knowledge Representation and Reasoning, ASP benefits from its simplicity, conciseness and rigorously defined semantics. These characteristics make ASP a straightforward way to develop formally verifiable programs. In the context of artificial intelligence (AI), the clarity of ASP programs lends itself to the construction of explainable, trustworthy AI. In support of these goals, my research is concerned with extending the theory and tools supporting the verification of ASP progams.

Recently, Large Language Models (LLMs) have showcased their potential in various natural language processing tasks, including code generation. However, while significant progress has been made in adapting LLMs to generate code for several imperative programming languages and tasks, there remains a notable gap in their application to declarative formalisms, such as Answer Set Programming (ASP). In this paper, we move a step towards exploring the capabilities of LLMs for ASP code generation. First, we perform a systematic evaluation of several state-of-the-art LLMs. Despite their power in terms of number of parameters, training data and computational resources, empirical results demonstrate inadequate performances in generating correct ASP programs. Therefore, we propose LLASP, a fine-tuned lightweight model specifically trained to encode fundamental ASP program patterns. To this aim, we create an ad-hoc dataset covering a wide variety of fundamental problem specifications that can be encoded in ASP. Our experiments demonstrate that the quality of ASP programs generated by LLASP is remarkable. This holds true not only when compared to the non-fine-tuned counterpart but also when compared to the majority of eager LLM candidates, particularly from a semantic perspective. All the code and data used to perform the experiments are publicly available: https://github.com/EricaCoppolillo/LLASP.

Answer Set Programming (ASP) is a well-known declarative problem solving approach based on nonmonotonic logic programs, which has been successfully applied to a wide range of applications in artificial intelligence and beyond. To address the needs of modern applications, HEX-programs were introduced as an extension of ASP with external atoms for accessing information outside programs via an API style bi-directional interface mechanism. To evaluate such programs, conflict-driving learning algorithms for SAT and ASP solving have been extended in order to capture the semantics of external atoms. However, a drawback of the state-of-the-art approach is that external atoms are only evaluated under complete assignments (i.e., input to the external source) while in practice, their values often can be determined already based on partial assignments alone (i.e., from incomplete input to the external source). This prevents early backtracking in case of conflicts, and hinders more efficient evaluation of HEX-programs. We thus extend the notion of external atoms to allow for three-valued evaluation under partial assignments, while the two-valued semantics of the overall HEX-formalism remains unchanged. This paves the way for three enhancements: first, to evaluate external sources at any point during model search, which can trigger learning knowledge about the source behavior and/or early backtracking in the spirit of theory propagation in SAT modulo theories (SMT). Second, to optimize the knowledge learned in terms of so-called nogoods, which roughly speaking are impossible input-output configurations. Shrinking nogoods to their relevant input part leads to more effective search space pruning. And third, to make a necessary minimality check of candidate answer sets more efficient by exploiting early external evaluation calls. As this check usually accounts for a large share of the total runtime, optimization is here particularly important. We further present an experimental evaluation of an implementation of a novel HEX-algorithm that incorporates these enhancements using a benchmark suite. Our results demonstrate a clear efficiency gain over the state-of-the-art HEX-solver for the benchmarks, and provide insights regarding the most effective combinations of solver configurations.

<p><strong>This thesis investigates how selected Science Fiction (SF) narratives, where Artificial Intelligence (AI) plays a central role, serve as sites for exploring and critiquing Western legal paradigms as they encounter emergent AI technologies. Through the Jurisprudential Thought-Experiment (JTE) reading, an original analytical framework developed in this research, SF is positioned not merely as a reflection of societal anxieties but as a counterfactual jurisprudential laboratory, where legal paradigms are critically examined through a stress-test exercise. The thesis distinguishes between the Macro-Legal Paradigm of Juridical Humanism, which constitutes a comprehensive legal-philosophical framework placing human beings at the centre of legal systems, and micro-legal paradigms, which address specific constructs such as personhood, intellectual property, legal reasoning, and the secularity of legal systems. Through a JTE reading, the thesis evaluates how SF narratives engage with these paradigms, determining whether AI-driven legal challenges lead to a paradigm stretch, wherein legal frameworks adapt while retaining their foundational commitments, or a paradigm shift, wherein core jurisprudential assumptions become untenable, requiring a parallel paradigm to claim the dominant position.</strong></p><p>The selected narratives include The Life Cycle of Software Objects, The Great Silence, and The Evolution of Science by American-Chinese author Ted Chiang; La Vía del Futuro by Bolivian writer Edmundo Paz Soldán; Homine Ex Machina by Spanish author Carlos Sisi; The Night Sessions by Scottish science fiction writer Ken MacLeod; and Plague Birds by American author Jason Sanford. These texts explore the limits of Western legal paradigms when confronted with fictional legal novums, such as AI entities seeking legal recognition, algorithmic creativity challenging copyright law, and AI-driven legal reasoning questioning the interpretive legitimacy of human legal reasoning. By introducing these legal novums, SF generates a space for legal paradigms to be critiqued, stress-tested, and sometimes reimagined. This thesis integrates two core theoretical frameworks: Jurisprudential Imaginaries, adapted from Sheila Jasanoff’s sociotechnical imaginaries, and the analytical utility of Thomas Kuhn’s paradigms and paradigm shifts. By bridging legal scholarship, Science and Technology Studies (STS) SF studies, and the Law and Literature movement, the thesis argues that SF extends beyond its traditional role as a repository of legal anxieties, functioning instead as a jurisprudentially engaged genre that interrogates the co-production of law and technology.</p><p>This study ultimately underscores SF’s jurisprudential potential as a tool for fostering critical reflection and dialogue—a jurisprudential laboratory where legal paradigms are stress-tested against counterfactual scenarios. By estranging readers from familiar legal constructs and presenting alternative yet coherent frameworks, the selected SF narratives invite a deeper interrogation of the principles underpinning Western legal traditions. Drawing on Sheila Jasanoff’s theory of co-production, the thesis situates SF also as a dynamic space of co-production where legal and technological imaginaries intersect. This interplay between estrangement and critique highlights SF’s unique capacity to illuminate legal paradigms' limitations, adaptability, and ethical dimensions, enriching contemporary discussions on AI and law.</p>

Answer Set Programming (ASP) is a rule-based language rooted in traditional Logic Programming, Databases, Knowledge Representation, and Nonmonotonic Reasoning. It offers a flexible language for declarative problem solving, with support of efficient general-purpose solvers and reasoners. The larger part of this article provides an introduction to ASP, with a historical perspective, a definition of the core language, a guideline to knowledge representation, and an overview of existing ASP solvers. One part focuses on one commonly used feature: aggregates and generalized atoms. The inclusion of aggregates in ASP (and Logic Programming at large) has long been motivated, however there are some issues with semantics to be addressed, in particular when aggregates occur in recursive definitions. Very similar considerations are needed when coupling ASP with other formalisms, which we collectively refer to as “generalized atoms”. An overview of these semantic challenges and proposals for addressing them is provided, along with an overview of complexity results and system support.

LAW IN DIGITAL REALITY

Ontologies are pervading many areas of knowledge representation and management. To date, most research efforts have been spent on the development of sufficiently expressive languages for the representation and querying of ontologies; however, querying efficiency has received attention only recently, especially for ontologies referring to large amounts of data. In fact, it is still uncertain how reasoning tasks will scale when applied on massive amounts of data. This work is a first step toward this setting: it first shows that Resource Description Framework(Schema) [RDF(S)] ontologies can be expressed, without loss of semantics, into Answer Set Programming (ASP). Then, based on a previous result showing that the SPARQL query language (a candidate W3C recommendation for RDF(S) ontologies) can be mapped to a rule-based language, it shows that efficient querying of big ontologies can be accomplished with a database oriented extension of the well known ASP system DLV, which we recently developed. Results reported in the article show that our proposed framework is promising for the improvement of both scalability and expressiveness of available RDF(S) storage and query systems.

Points-to information is essential in software engineering, including compiler optimization, instruction-level parallelism, program verification, and so on. The propagating of points-to information along paths of a program can be viewed as the “frame problem” in a dynamic world. As a primary knowledge representing and reasoning tool in Artificial Intelligence, Answer Set Programming (ASP) provides a natural and concise way to express the frame problem. We present a collection of ASP rules to model the propagating of points-to information along paths of an object-oriented program. With these rules and basic facts of a program, points-to information at each program point can be computed easily. This makes it possible to acquire useful information for program analysis through existing ASP solvers.KeywordsAnswer Set Programmingknowledge representing and reasoningpoints-to analysisobject-oriented language