The Winograd Schema Challenge—a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge—was proposed by Hector Levesque in 2011. By 2019, a number of AI systems, based on large pre-trained transformer-based language models and fine-tuned on these kinds of problems, achieved better than 90% accuracy. In this paper, we review the history of the Winograd Schema Challenge and discuss the lasting contributions of the flurry of research that has taken place on the WSC in the last decade. We discuss the significance of various datasets developed for WSC, and the research community's deeper understanding of the role of surrogate tasks in assessing the intelligence of an AI system.

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.

This paper describes the Winograd Schema Challenge (WSC), which has been suggested as an alternative to the Turing Test and as a means of measuring progress in commonsense reasoning. A competition based on the WSC has been organized and announced to the AI research community. The WSC is of special interest to the AI applications community and we encourage its members to participate.

As part of a larger effort to explore the feasibility of automating the translation of regulatory text to formal, executable rules, we have developed automated methods to classify regulatory paragraphs into various categories of interest, including regulation type and reference structure. We achieve F1 scores of at least .8 for most categories.

This paper presents a theory expressed in first-order logic for describing and supporting inference about action, knowledge, planning, and communication, in an egalitarian multi-agent setting. The underlying ontology of the theory uses a situationbased temporal model and a possible-worlds model of knowledge. It supports plans and communications of a very general kind, both informative communications and requests. Communications may refer to states of the world or states of knowledge in the past, present, or future. We demonstrate that the theory is powerful enough to represent several interesting multi-agent planning problems and to justify their solutions. We have proven that the theory of knowledge, communication, and planning is consistent with a broad range of physical theories, despite the existence of a number of potential paradoxes.