Cycorp (United States)

Abstract In this paper, we consider some contributions to the model theory of the logic of formal inconsistency $\mathsf{QmbC}$ as a reply to Walter Carnielli, Marcelo Coniglio, Rodrigo Podiacki and Tarcísio Rodrigues’ call for a ‘wider model theory.’ This call demands that we align the practices and techniques of model theory for logics of formal inconsistency (LFIs) as closely as possible with those employed in classical model theory. The key result is a proof that the Keisler–Shelah isomorphism theorem holds for $\mathsf{QmbC}$, i.e. that the strong elementary equivalence of two $\mathsf{QmbC}$ models $\mathfrak{A}$ and $\mathfrak{B}$ is equivalent to them having strongly isomorphic ultrapowers. As intermediate steps, we introduce some notions of model-theoretic equivalence between $\mathsf{QmbC}$ models, explicitly prove Łoś’ theorem and introduce a useful technique of model-theoretic ‘atomization’ in which the satisfaction sets of non-deterministically evaluated formulae are associated with new predicates. Finally, we consider some of the extensions of $\mathsf{QmbC}$, explicitly showing that Keisler–Shelah holds for $\mathsf{QCi}$ and suggesting that it holds of extensions like $\mathsf{QCila}$ and $\mathsf{QCia}$ as well.

Commonsense reasoning at scale is a critical problem for modern cognitive systems. Large theories have millions of axioms, but only a handful are relevant for answering a given goal query. Irrelevant axioms increase the search space, overwhelming unoptimized inference engines in large theories. Therefore, methods that help in identifying useful inference paths are an essential part of large cognitive systems. In this paper, we use retrograde analysis to build a database of proof paths that lead to at least one successful proof. This database helps the inference engine identify more productive parts of the search space. A heuristic based on this approach is used to order nodes during a search. We study the efficacy of this approach on hundreds of queries from the Cyc KB. Empirical results show that this approach leads to significant reduction in inference time.

We often understand something only after we’ve had to teach or explain it to someone else. Learning-by-teaching (LBT) systems exploit this phenomenon by playing the role of tutee. BELLA, our sixth-grade mathematics LBT systems, departs from other LTB systems in several ways: (1) It was built not from scratch but by very slightly extending the ontology and knowledge base of an existing large AI system, Cyc. (2) The “teachable agent”—Elle—begins not with a tabula rasa but rather with an understanding of the domain content which is close to the human student’s. (3) Most importantly, Elle never actually learns anything directly from the human tutor! Instead, there is a super-agent (Cyc) which already knows the domain content extremely well. BELLA builds up a mental model of the human student by observing them interact with Elle. It uses that Socratically to decide what Elle’s current mental model should be (what concepts and skills Elle should already know, and what sorts of mistakes it should make) so as to best help the user to overcome their current confusions. All changes to the Elle model are made by BELLA, not by the user—the only learning going on is BELLA learning more about the user—but from the user’s point of view it often appears as though Elle were attending to them and learning from them. Our main hypothesis is that this may prove to be a particularly powerful and effective illusion to maintain.

Abstract We present a procedure for translating predicate logic into English, which generates both referring and non-referring expressions using a dynamically updated context representation. The system treats referring and non-referring expressions within a unified framework, capturing their common properties – both bound and referential anaphora require an accessible antedecent – and the special properties of non-referring expressions: Non-referring expressions are introduced with quantificational determiners, and correspond to short-term discourse referents.Keywordsnatural language generationdynamic semanticspredicate logicquantificationanaphora

<div class="htmlview paragraph">ULTALITE<sup>®</sup> is a patented low cost Aluminium Metal Matrix Composite (Al-MMC) produced using flyash particles as the reinforcement particulate. The material is proven to possess higher stiffness, strength and wear resistance relative to the monolithic matrix alloy both at room and elevated temperatures.</div> <div class="htmlview paragraph">Cast Iron has been utilised in brake drums and disc rotors throughout the history of Automobile manufacturing. High density, rigorous machining due to imprecise casting and consequently, substantial production cycle times are few of the inherited drawbacks in utilising cast iron for brake drums. Utilisation of a material such as this is undoubtedly invaluable in eliminating majority of such shortcomings.</div> <div class="htmlview paragraph">In this paper, our focus is on the performance of automotive brake drums produced with this new material. Brake dynamometer test results are presented and analysed substantiating the suitability of the technology. Brake drums for testing purposes were produced by a squeeze casting method.</div> <div class="htmlview paragraph">Brake dynamometer tests were conducted in accordance with the SAE J2522 standard. The new AL-MMC drums with standard linings exhibit equivalent or better performance comparatively to OE Cast Iron drums at all times. Heat dissipation characteristics of these Al-MMC based brake drums are outstanding compared to OE Cast Iron brake drums and this is further proven by the Brake Dynamometer test results. Wear test results indicate superior wear characteristics compared to OE Cast Iron brake drums.</div> <div class="htmlview paragraph">Components made from this new material also possess characteristics such as less machining, stringent tolerances, less production time and substantial weight reductions partly due to the die casting fabrication process. Considering the tests results and evident benefits, we demonstrate that the investigated Al-MMC based brake drums are a potential substitute for OE Cast Iron brake drums.</div>

Over the last 21 years, we’ve spent almost a person-millenium producing Cyc, an axiomatization of general human knowledge. Though still far from complete, Cyc contains over three million axioms. The need to express the range of things a person knows has led us to ever more expressive representation languages – currently we use an nth order predicate calculus with an overlay of contexts which are themselves first class objects. These pressures and others (e.g., elaboration tolerance) have driven us against numerous sorts of ”scaling up” problems. In this talk I will briefly describe Cyc, the processes by which new axioms are added and deleted, applications of it, etc., but I will focus on some of these scaling up issues and approaches we have taken – and plan to take – to keep inference fast enough and to keep contradictions from being more than a nuisance.

Eliciting complex logical rules directly from logic-naive subject matter experts (SMEs) is a challenging knowledge capture task. We describe a large-scale experiment to evaluate tools designed to produce SME-authored rule bases. We assess the quality of the rule bases with respect to the: 1) performance on the addressed functional task (military course of action (COA) critiquing); and 2) intrinsic knowledge representation quality. In the course of this assessment, we note both strengths and weaknesses in the state of the art, and accordingly suggest some foci for future development in this important technology area.

Abstract In this article we present an overview of knowledge‐based systems. We begin by discussing the basic characteristics of knowledge base rules and present an example comparing a toy knowledge base to an equivalent procedural version of the same code. We then discuss the applications for which it makes sense to use knowledge‐based technology and the advantages offered by reuse of a general‐purpose upper ontology . Next we describe important research and applications in both knowledge representation and knowledge‐based systems. We then describe how work in philosophy and other research has had an impact on knowledge‐based systems. We conclude with thoughts on where the field is heading.

The Cyc lexicon is a broad-coverage lexicon which is being constructed as a component of the Cyc KB a very large repository of common sense knowledge. Building and maintaining a large lexicon in conjunction with a knowledge base and inference engine is a much different task than building a stand-alone lexicon, or a lexicon which relies on a small internal or external ontology. In building the Cyc lexicon, we have been concerned not with demonstrating or testing theoretical claims, but with creating a practical tool, since Cyc-based applications demand a certain level of functionality from the lexicon. As such, our work is theory-driven only to the extent that a particular theory proves useful to us. In this chapter we discuss a number of issues which have arisen in developing the Cyc lexicon, including the feasibility of taking advantage of semantic classification schemes as a shortcut to hand-entering lexical information; distinguishing between world and lexical knowledge; and providing a level of semantic detail in the lexicon which is compatible with the expressiveness of Cyc’s internal representation language.KeywordsLexical EntryWord SenseParticipant RoleMass NounCount NounThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.