Synthetic datasets constructed from formal languages allow fine-grained examination of the learning and generalization capabilities of machine learning systems for sequence classification. This article presents a new benchmark for machine learning systems on sequence classification called MLRegTest, which contains training, development, and test sets from 1,800 regular languages. Different kinds of formal languages represent different kinds of long-distance dependencies, and correctly identifying long-distance dependencies in sequences is a known challenge for ML systems to generalize successfully. MLRegTest organizes its languages according to their logical complexity (monadic second order, first order, propositional, or restricted propositional) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages, and therefore to understand the capacities of different ML systems to learn such long-distance dependencies. Finally, the performance of different neural networks (simple RNN, LSTM, GRU, transformer) on MLRegTest is examined. The main conclusion is that performance depends significantly on the kind of test set, the class of language, and the neural network architecture.

Abstract Knowledge representation and reasoning (KRR) systems describe and reason with complex concepts and relations in the form of facts and rules. Unfortunately, wide deployment of KRR systems runs into the problem that domain experts have great difficulty constructing correct logical representations of their domain knowledge. Knowledge engineers can help with this construction process, but there is a deficit of such specialists. The earlier Knowledge Authoring Logic Machine (KALM) based on Controlled Natural Language (CNL) was shown to have very high accuracy for authoring facts and questions. More recently, KALMFL, a successor of KALM, replaced CNL with factual English, which is much less restrictive and requires very little training from users. However, KALMFL has limitations in representing certain types of knowledge, such as authoring rules for multi-step reasoning or understanding actions with timestamps. To address these limitations, we propose KALMRA to enable authoring of rules and actions. Our evaluation using the UTI guidelines benchmark shows that KALMRA achieves a high level of correctness (100%) on rule authoring. When used for authoring and reasoning with actions, KALMRA achieves more than 99.3% correctness on the bAbI benchmark, demonstrating its effectiveness in more sophisticated KRR jobs. Finally, we illustrate the logical reasoning capabilities of KALMRA by drawing attention to the problems faced by the recently made famous AI, ChatGPT.

Abstract This special issue of Theory and Practice of Logic Programming consists of extended versions of five selected papers from the 3rd International Joint Conference on Rules and Reasoning (RuleML+RR 2019). RuleML+RR 2019 was held in conjunction with the 5th Global Conference on Artificial Intelligence, GCAI 2019, as part of the Bolzano Rules and Artificial INtelligence Summit in Bolzano, Italy, from 17 to 19 of September 2019.

Abstract The inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace.Knowledge and query authoringusing natural language, especiallycontrollednatural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced theKALMsystem (Knowledge Authoring Logic Machine) that supports knowledge authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introducesKALM-QA(KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, calledMetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.

Knowledge representation and reasoning (KRR) is key to the vision of the intelligent Web. Unfortunately, wide deployment of KRR is hindered by the difficulty in specifying the requisite knowledge, which requires skills that most domain experts lack. A way around this problem could be to acquire knowledge automatically from documents. The difficulty is that, KRR requires high-precision knowledge and is sensitive even to small amounts of errors. Although most automatic information extraction systems developed for general text understandings have achieved remarkable results, their accuracy is still woefully inadequate for logical reasoning. A promising alternative is to ask the domain experts to author knowledge in Controlled Natural Language (CNL). Nonetheless, the quality of knowledge construction even through CNL is still grossly inadequate, the main obstacle being the multiplicity of ways the same information can be described even in a controlled language. Our previous work addressed the problem of high accuracy knowledge authoring for KRR from CNL documents by introducing the Knowledge Authoring Logic Machine (KALM). This paper develops the query aspect of KALM with the aim of getting high precision answers to CNL questions against previously authored knowledge and is tolerant to linguistic variations in the queries. To make queries more expressive and easier to formulate, we propose a hybrid CNL, i.e., a CNL with elements borrowed from formal query languages. We show that KALM achieves superior accuracy in semantic parsing of such queries.

The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages, and standards for rule technologies. It has evolved from an annual series of international workshops since 2002, international conferences in 2005 and 2006, and international symposia since 2007. It is the flagship event of the Rule Markup and Modeling Initiative (RuleML, http://ruleml.org ), a nonprofit umbrella organization of several technical groups from academia, industry, and government working on rule technology and its applications. RuleML is the leading conference to build bridges between academia and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rules engines/business rules management systems; Semantic Web rule languages and rule standards (e.g., RuleML, SWRL, RIF, PRR, SBVR, DMN, CL, Prolog); rule-based event processing languages and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules.

Abstract Word puzzles and the problem of their representations in logic languages have received considerable attention in the last decade (Ponnuruet al. 2004; Shapiro 2011; Baral and Dzifcak 2012; Schwitter 2013). Of special interest is the problem of generating such representations directly from natural language (NL) or controlled natural language (CNL). An interesting variation of this problem, and to the best of our knowledge, scarcely explored variation in this context, is when the input information is inconsistent. In such situations, the existing encodings of word puzzles produce inconsistent representations and break down. In this paper, we bring the well-known type of paraconsistent logics, calledAnnotated Predicate Calculus(APC) (Kifer and Lozinskii 1992), to bear on the problem. We introduce a new kind of non-monotonic semantics for APC, calledconsistency preferred stable modelsand argue that it makes APC into a suitable platform for dealing with inconsistency in word puzzles and, more generally, in NL sentences. We also devise a number of general principles to help the user choose among the different representations of NL sentences, which might seem equivalent but, in fact, behave differently when inconsistent information is taken into account. These principles can be incorporated into existing CNL translators, such as Attempto Controlled English (ACE) (Fuchset al. 2008) and PENG Light (White and Schwitter 2009). Finally, we show that APC with the consistency preferred stable model semantics can be equivalently embedded in ASP with preferences over stable models, and we use this embedding to implement this version of APC in Clingo (Gebseret al. 2011) and its Asprin add-on (Brewkaet al. 2015).

Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams.

Transaction Logic is a logic for representing declarative and procedural knowledge in logic programming, databases, and AI. It has been successful in areas as diverse as workflows and Web services, security policies, AI planning, reasoning about actions, and more. Although a number of implementations of Transaction Logic exist, none is logically complete due to the inherent difficulty and time/space complexity of such implementations. In this paper we attack this problem by first introducing a logically complete tabling evaluation strategy for Transaction Logic and then describing a series of optimizations, which make this algorithm practical. In support of our arguments, we present a performance evaluation study of six different implementations of this algorithm, each successively adopting our optimizations. The study suggest that the tabling algorithm can scale well both in time and space. We also discuss ideas that could improve the performance further.

The Semantic Web initiative has led to an upsurge of the interest in rules as a general and powerful way of processing, combining, and analyzing semantic information. Since several of the technologies underlying rule-based systems are already quite mature, it is important to understand how such systems might perform on the Web scale. OpenRuleBench is a suite of benchmarks for analyzing the performance and scalability of different rule engines. Currently the study spans five different technologies and eleven systems, but OpenRuleBench is an open community resource, and contributions from the community are welcome. In this paper, we describe the tested systems and technologies, the methodology used in testing, and analyze the results.

Dialog interaction in conversational applications is subordinated to the goal of completing a domain-specific task. In this paper we present a basic architecture, a knowledge representation system, and a planning algorithm for dialogue management that decouples the interaction process from the planning task. In our system, the interaction is driven by the planner. We use logic programming, automatic planning and problem solving algorithms for representing information states and performing interaction management in the dialogue system. Our approach leverages recent advances in formalisms, inference engines, planning and problem solving, and is particularly suitable when implementing negotiation-intensive conversational applications.