LPMLN is a probabilistic extension of answer set programs with the weight scheme adapted from Markov Logic. We study the concept of strong equivalence in LPMLN, which is a useful mathematical tool for simplifying a part of an LPMLN program without looking at the rest of it. We show that the verification of strong equivalence in LPMLN can be reduced to equivalence checking in classical logic via a reduct and choice rules as well as to equivalence checking under the "soft" logic of here-and-there. The result allows us to leverage an answer set solver for LPMLN strong equivalence checking. The study also suggests us a few reformulations of the LPMLN semantics using choice rules, the logic of here-and-there, and classical logic.

From a set of technical drawings and expert knowledge, we automatically learn a parser to interpret such a drawing. This enables automatic reasoning and learning on top of a large database of technical drawings. In this work, we develop a similarity based search algorithm to help engineers and designers find or complete designs more easily and flexibly. This is part of an ongoing effort to build an automated engineering assistant. The proposed methods make use of both neural methods to learn to interpret images, and symbolic methods to learn to interpret the structure in the technical drawing and incorporate expert knowledge.

Answer Set Programming (ASP) is a famous logic language for knowledge representation, which has been really successful in the last years, as witnessed by the great interest into the development of efficient solvers for ASP. Yet, the great request of resources for certain types of problems, as the planning ones, still constitutes a big limitation for problem solving. Particularly, in the case the program is grounded before the resolving phase, an exponential blow up of the grounding can generate a huge ground file, infeasible for single machines with limited resources, thus preventing even the discovering of a single non-optimal solution. To address this problem, in this paper we present a distributed approach to ASP solving, exploiting distributed computation benefits in order to overcome the just explained limitations. The here presented tool, which is called Distributed Answer Set Coloring (DASC), is a pure solver based on the well-known Graph Coloring algorithm. DASC is part of a bigger project aiming to bring logic programming into a distributed system, started in 2017 by Federico Igne with mASPreduce and continued in 2018 by Pietro Totis with a distributed grounder. In this paper we present a low level implementation of the Graph Coloring algorithm, via the Boost and MPI libraries for C++. Finally, we provide a few results of the very first working version of our tool, at the moment without any strong optimization or heuristic.

An intelligent system must be capable of performing automated reasoning as well as responding to the changing environment (for example, changing knowledge). To exhibit such an intelligent behavior, a machine needs to understand its environment as well be able to interact with it to achieve certain goals. For acting rationally, a machine must be able to obtain information and understand it. Knowledge Representation (KR) is an important step of automated reasoning, where the knowledge about the world is represented in a way such that a machine can understand and process. Also, it must be able to accommodate the changes about the world (i.e., the new or updated knowledge). Using the generated knowledge base about the world, an intelligent system should be able to do complex tasks like question-answering (QA), summarization, medical reasoning and many more.

In this thesis, we introduce a novel formal framework to represent and reason about qualitative direction and distance relations between extended objects using Answer Set Programming (ASP). We take Cardinal Directional Calculus (CDC) as a starting point and extend CDC with new sorts of constraints which involve defaults, preferences and negation. We call this extended version as nCDC. Then we further extend nCDC by augmenting qualitative distance relation and name this extension as nCDC+. For CDC, nCDC, nCDC+, we introduce an ASP-based general framework to solve consistency checking problems, address composition and inversion of qualitative spatial relations, infer unknown or missing relations between objects, and find a suitable configuration of objects which fulfills a given inquiry.

However the overall aim is not only important for equipping machines with capabilities of moral reasoning, but also for helping us to better understand morality through creating and testing computational models of ethical machines that follow a set of ideal ethical principles. Since the beginning of this century there were several attempts for implementing ethical decision making into intelligent autonomous agents using different approaches. But, no fully descriptive and widely accepted model of moral judgment and decision-making exists. In this work we propose a hybrid logic-based approach for modeling ethical machines, particularly ethical chatbots. As a matter of fact the potential of logic programming (LP) to model moral machines was envisioned by Pereira and Saptawijaya [15].

Answer Set Programming (ASP) [3] has been shown to be especia lly effective on search and optimization problems whose decision versions are in the class NP, includ ing many problems of practical interest [9, 6]. Despite the ease of modeling and the demonstrated pot ential of ASP, using it poses challenges. In particular, it is unlikely a single solver will emerge tha t would uniformly outperform other solvers. Consequently, selecting a solver for an instance may mean th e difference between solving the problem within an acceptable time and having the solver run "forever ." To address the problem, solver selection, portfolio solving, and automated solver parameter configur ation have all been extensively studied [17, 10, 14, 16, 12].

Mutual exclusion (mutex) can be traced back to concurrency control, which refers to the condition that prevents simultaneous accesses to a shared resource. In knowledge representation, they specify the constraints that some properties cannot hold at the same time. For example, an object cannot be at different locations at the same time. These constraints frequently occur in applications from model-checking problems in computer-aided verification [2], computer vision [12, 17], graph algorithms [11], and AI planning [14]. The goal of this paper is to develop a graph-theoretic technique for compactly encoding large sets of mutex constraints and apply it to planning in ASP . We do his by focusing on domain-independent AI planning as started out by SA TPlan [10]. That is, we will first obtain an ASP planner by a straightforward translation from SA TPlan and then study how to encode mutex constraints compactly for the planner. In SA T/ASP planning, mutex constraints are specified by formulas/rules that, for any state (which involves a time step, also called a layer in this paper), the actions with conflicting preconditions or effects, and the fluents that are inferred to be conflicting, are mutually exclusive. A naive encoding of these constraints can certainly generate enough rules to overwhelm the underlying solver for large planning instances.

By incorporating the methods of Answer Set Programming (ASP) and Markov Logic Networks (MLN), LPMLN becomes a powerful tool for non-monotonic, inconsistent and uncertain knowledge representation and reasoning. To facilitate the applications and extend the understandings of LPMLN, we investigate the strong equivalences between LPMLN programs in this paper, which is regarded as an important property in the field of logic programming. In the field of ASP, two programs P and Q are strongly equivalent, iff for any ASP program R, the programs P and Q extended by R have the same stable models. In other words, an ASP program can be replaced by one of its strong equivalent without considering its context, which helps us to simplify logic programs, enhance inference engines, construct human-friendly knowledge bases etc. Since LPMLN is a combination of ASP and MLN, the notions of strong equivalences in LPMLN is quite different from that in ASP. Firstly, we present the notions of p-strong and w-strong equivalences between LPMLN programs. Secondly, we present a characterization of the notions by generalizing the SE-model approach in ASP. Finally, we show the use of strong equivalences in simplifying LPMLN programs, and present a sufficient and necessary syntactic condition that guarantees the strong equivalence between a single LPMLN rule and the empty program.

This tool uses an action language ALM to perform inferences on complex interactions of events described in narratives. The methodology used to implement the TEXT2 ALM system was originally outlined by Lierler, Inclezan, and Gelfond [13] via a manual process of converting a narrative to an ALM model. It relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, natural language processing and knowledge representation and reasoning. The effectiveness of system TEXT2 ALM is measured by its ability to correctly answer questions from the bAbI tasks published by Facebook Research in 2015. This tool matched or exceeded the performance of state-of-the-art machine learning methods in six of the seven tested tasks. We also illustrate that the TEXT2 ALM approach generalizes to a broader spectrum of narratives. 1 Introduction The field of Information Extraction (IE) is concerned with gathering snippets of meaning from text and storing the derived data in structured, machine interpretable form. Consider a sentence BBDO South in Atlanta, which handles corporate advertising for Georgia-Pacific, will assume additional duties for brands like Angel Soft, said Ken Haldin, a spokesman for Georgia-Pacific from Atlanta. A sample IE system that focuses on identifying organizations and their corporate locations may extract the following predicates from this sentence: locatedIn (BBDOSouth, Atlanta) locatedIn (GeorgiaPaci f ic, Atlanta) These predicates can then be stored either in a relational database or a logic program, and queried accordingly by well-known methods in computer science. Thus, IE allows us to turn unstructured data present in text into structured data easily accessible for automated querying. In this paper, we focus on an IE system that is capable of processing simple narratives with action verbs, in particular, verbs that express physical acts such as go, give, and put. Consider a sample narrative that we refer to as the JS discourse: John traveled to the hallway. We appreciate the insights from Michael Gelfond, Daniela Inclezan, Edward Wertz, and Y uanlin Zhang on their work on language ALM, the C OREALML IB library, and system CALM.