ABSTRACT. Automating commonsense reasoning, i.e., automating the human thought process, has been considered fiendishly difficult. It is widely believed that automation of commonsense reasoning is needed to build intelligent systems that can rival humans. We argue that answer set programming (ASP) along with its goal-directed implementation allows us to reach this automation goal. We discuss essential elements needed for automating the human thought process, and show how they are realized in ASP and the s(CASP) goal-directed ASP engine.

ABSTRACT. In this paper, we present a goal-directed proof procedure for ASP with abduction. Our proposed procedure in this paper is correct for any consistent abductive framework proposed in \cite{Kakas}. In other words, if the procedure succeeds, there is a set of hypotheses which satisfies a query, and if the procedure finitely fails, there is no such set. If we do not consider abducibles, this procedure is a goal-directed proof procedure for ASP as well. NOTE: This paper is an extended abstract of a paper with the title ``A Query Evaluation Method for Abductive Logic Programming'' that appeared in the Proceedings of the Joint International Conference and Symposium on Logic Programming (JICSLP'92), pp. 671 -- 685.

ABSTRACT. The context of the paper is developing logic-based components for hybrid -- machine learning plus logic -- commonsense question answering systems. The paper presents the main principles and several lessons learned from implementing an automated reasoner able to handle both undecidable exceptions and numerical confidences for full first order logic. Although the described reasoner is based on the resolution method, some of these lessons may be useful for the further development of ASP systems as well.

ABSTRACT. This paper showcases the use of Logical English, a logic programming language that allows for expressing rules and explanations in a controlled form of natural language, which can be interpreted by the s(CASP) reasoner. It demonstrates the possibility of representing and reasoning with legal values, unknown facts and time with carefully selected expressions of English that can be easily understood without technical training. This research has been developed in the context of the CrossJustice Project.

ABSTRACT. The original s(CASP) implementation is a stand-alone program implemented in Ciao Prolog. It reads the s(CASP) source using a dedicated parser, resolves the query embedded in the source and emits the results in a format dictated by commandline options. Typical applications require composing a s(CASP) program, solving a query and reasoning about the \textit{bindings}, \textit{model} and/or \textit{justification}. This is often done in some external language, e.g., Python. In this paper we propose a closer integration with Prolog. The s(CASP) program is simply a Prolog program that has to respect certain constraints. The scasp library can be used to solve a query using s(CASP) semantics, making the bindings available in the normal Prolog way and providing access to the model and justification as Prolog terms. This way, we can exploits Prolog's power for manipulating (Prolog) terms for construction the s(CASP) program and interpreting the results.

ABSTRACT. The formal representation of a legal text to automatize reasoning about them is well known in literature and is recently gaining much attention thanks to the interest in the so-called smart contracts, and to autonomous decisions by public administrations [8,4,11]. For deterministic rules, there are several proposals, often based on logic-based programming languages [9,10]. However, none of the existing proposals are able to represent the ambiguity and/or administrative discretion present in contracts and/or applicable legislation, e.g., force majeure. This paper is an extended abstract of [3], where we present a framework, called s(LAW), that allows for modeling legal rules involving ambiguity, and supports reasoning and inferring conclusions based on them.

ABSTRACT. In recent years Web browsers are becoming closer and closer to full-fledged computing platforms. Ciao Prolog currently includes a browser-based playground which allows editing and running programs locally in the browser with no need for server-side interaction. The playground is built from reusable components, and allows easily embedding runnable Prolog programs in web pages and documents. These components can also be easily used for the development of specific, fully browser-based applications. The purpose of this paper is to present a browser-based environment for developing s(CASP) programs, based on the Ciao Prolog playground and its components. This s(CASP) playground thus runs locally on the browser with no need for interaction with a server beyond code download. After briefly introducing s(CASP) and Ciao Prolog, we provide an overview of the architecture of the Ciao playground, based on a compilation of the Ciao engine to the WebAssembly platform, describe the steps involved in its adaptation to create the s(CASP) playground, and present some of its capabilities. These include editing and running s(CASP) programs, sharing them, obtaining (sets of) answers, and visualizing and exploring explanations.

ABSTRACT. The paper studies defeasible reasoning in rule-based systems, in particular about legal norms and contracts. We identify rule modifiers that specify how rules interact and how they can be overridden. We then define rule transformations that eliminate these modifiers, leading in the end to a translation of rules to formulas. For reasoning with and about rules, we contrast two approaches, one in a classical logic with SMT solvers, which is only briefly sketched, and one using non-monotonic logic with Answer Set Programming solvers, described in more detail.

ABSTRACT. We present a novel application of Goal-Directed Answer Set Programming that digitizes the model aircraft operator’s compliance verification against the Academy of Model Aircrafts (AMA) safety code. The AMA safety code regulates how AMA flyers operate Unmanned Aerial Vehicles (UAVs) for limited recreational purposes. Flying drones and their operators are subject to various rules before and after the operation of the aircraft to ensure safe flights. In this paper, we leverage Goal-Directed Answer Set Programming to encode the AMA safety code and automate compliance checks. To check compliance, we use the s(CASP) which is a goal-directed ASP engine. By using s(CASP) the operators can easily check for violations and obtain a justification tree explaining the cause of the violations in human-readable natural language. We develop a front end questionnaire interface that accepts various conditions and use the backend s(CASP) engine to evaluate whether the conditions adhere to the regulations. We also leverage s(CASP) implemented in SWI-Prolog, where SWI-Prolog exposes the reasoning capabilities of s(CASP) as a REST service. To the best of our knowledge, this is the first application of ASP in the AMA and Avionics Compliance and Certification space.

ABSTRACT. In AI research, Relational Reinforcement Learning (RRL) is a vastly discussed domain that combines reinforcement learning with relational learning or inductive learning. One of the key challenges of inductive learning through rewards and action is to learn the relations incrementally. In other words, how an agent can closely mimic the human learning process. Where we, humans, start with a very naive belief about a concept and gradually update it over time to a more concrete hypothesis. In this paper, we address this challenge and show that an automatic theory revision component can be developed efficiently that can update the existing hypothesis based on the rewards the agent collects by applying it. We present a symbolic reinforcement learning framework with the automatic theory revision component for incremental learning. This theory revision component would not be possible to build without the help of a goal-directed execution engine of answer set programming (ASP) - s(CASP). The current work has demonstrated a proof of concept about the RL framework and we are still working on it.

ABSTRACT. We present a model checker for Linear Temporal Logic using Goal-Directed Answer Set Programming under Costable model semantics (CoASP). Costable model semantics allows for positive loops to succeed, unlike Stable model semantics where positive loops fail. Therefore, by using the Costable model semantics, LTL formulas involving the G and R operator can be proved coinductively.

ABSTRACT. In this paper, I briefly describe the summary of my work titled - Hybrid Neuro-Symbolic Approach for Text-Based Games using Inductive Logic Programming. Text-based games (TBGs) have emerged as an important test-bed, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning. A key challenge for agents solving this task is to generalize across multiple games and shows good results on both seen and unseen objects. To tackle these issues, we have designed a hybrid neuro-symbolic framework for TBGs that uses symbolic reasoning along with the neural RL model. We also use WordNet as an external commonsense knowledge source to bring information to generalize the hypothesis. We have tested our work on different settings on TWC games and showed that the agents that incorporate the neuro-symbolic hybrid approach with the generalized rules outperform the baseline agents. 

ABSTRACT. This paper describes Blawx, a prototype web-based user-friendly Rules as Code tool, powered by a goal-directed answer set programming. The paper briefly describes Rules as Code, and introduces desireable qualities for Rules as Code tools. It provides justifications for Blawx’s implementation of the Google Blockly library, and the s(CASP) reasoning system. It then provides a step-by-step tour of how Blawx allows a user to generate an answer set program representing their understanding of a statute, and use that encoding to power an application. The paper concludes with a brief discussion of the current short term and anticipated long-term development objectives for Blawx.

ABSTRACT. Panel discussion with TBA