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An Introduction to SNePS 3
 Conceptual Structures: Logical, Linguistic, and Computational Issues. Lecture Notes in Artificial Intelligence 1867
, 2000
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Mixed depth representations for dialog processing
 In Proceedings of the Cognitive Science Society
, 1998
"... We describe our work on developing a general purpose tutoring system that will allow students to practice their decisionmaking skills in a number of domains. The tutoring system, B2, supports mixedinitiative natural language interaction. The natural language processing and knowledge representation ..."
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Cited by 10 (5 self)
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We describe our work on developing a general purpose tutoring system that will allow students to practice their decisionmaking skills in a number of domains. The tutoring system, B2, supports mixedinitiative natural language interaction. The natural language processing and knowledge representation components are also general purposewhich leads to a tradeo between the limitations of super cial processing and syntactic representations and the di culty of deeper methods and conceptual representations. Students ' utterances may be short and ambiguous, requiring extensive reasoning about the domain or the discourse model to fully resolve. However, full disambiguation is rarely necessary. Our solution is to use a mixeddepth representation, one that encodes syntactic and conceptual information in the same structure. As a result, we can use the same representation framework to produce a detailed representation of requests (which tend to be wellspeci ed) and to produce a partial representation of questions (which tend to require more inference about the context). Moreover, the representations use the same knowledge representation framework that is used to reason about discourse processing and domain informationso that the system can reason with (and about) the utterances, if necessary.
A logic of arbitrary and indefinite objects
 Principles of Knowledge Representation and Reasoning: Proceedings of the Ninth International Conference (KR 2004
, 2004
"... A Logic of Arbitrary and Indefinite Objects, LA, has been developed as the logic for knowledge representation and reasoning systems designed to support natural language understanding and generation, and commonsense reasoning. The motivations for the design of LA are given, along with an informal int ..."
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Cited by 9 (3 self)
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A Logic of Arbitrary and Indefinite Objects, LA, has been developed as the logic for knowledge representation and reasoning systems designed to support natural language understanding and generation, and commonsense reasoning. The motivations for the design of LA are given, along with an informal introduction to the theory of arbitrary and indefinite objects, and to LA itself. LA is then formally defined by presenting its syntax, proof theory, and semantics, which are given via a translation scheme between LA and the standard classical FirstOrder Predicate Logic. Soundness is proved. The completeness theorem for LA is stated, and its proof is sketched. LA is being implemented as the logic of SNePS 3, the latest member of the SNePS family of Knowledge Representation and Reasoning systems. Motivations
A Propositional Semantic Network with Structured Variables for Natural Language Processing
 In Proceedings of the Sixth Australian Joint Conference on Artificial Intelligence
"... This paper suggests some goals for knowledge representation for natural language processing: natural form, conceptual completeness, structure sharing. To address these goals, an augmentation to the representation of variables so that variables are not atomic terms is suggested. This leads to an ext ..."
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Cited by 8 (6 self)
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This paper suggests some goals for knowledge representation for natural language processing: natural form, conceptual completeness, structure sharing. To address these goals, an augmentation to the representation of variables so that variables are not atomic terms is suggested. This leads to an extended, more \natural " representation language whose use and representations are consistent with the use of variables in natural language. It is shown how this representation simplies the representation and use of complex noun phrases and resolves some representational diculties with sentences involving nonlinear quanti er scoping, in particular, donkey sentences. 1
Formalizing English
 International Journal of Expert Systems
, 1996
"... The use of logic for knowledge representation and reasoning systems is controversial. There are, indeed, several ways that standard First Order Predicate Logic is inappropriate for modelling natural language understanding and commonsense reasoning. However, a more appropriate logic can be designed. ..."
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Cited by 5 (1 self)
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The use of logic for knowledge representation and reasoning systems is controversial. There are, indeed, several ways that standard First Order Predicate Logic is inappropriate for modelling natural language understanding and commonsense reasoning. However, a more appropriate logic can be designed. This paper presents several aspects of such a logic. Keywords: Knowledge representation; reasoning; natural language; commonsense reasoning; logic. 1 Introduction My colleagues, students, and I have been engaged in a longterm project to build a natural language using intelligent agent. While our approach to natural language understanding (NLU) and commonsense reasoning (CSR) has been logicbased, we have thought that the logics developed for metamathematics are not, necessarily, the best ones for our purpose. Instead, we have designed new logics, better suited for NLU and CSR. The current version of these logics constitutes the formal language and inference mechanism of the knowledge repr...
Inference Graphs: A Roadmap
"... Logical inference is one approach to implementing the reasoning component of a cognitive system. Inference graphs are a method for natural deduction inference which, uniquely in logicbased cognitive systems, use concurrency to reason about multiple possible ways to solve a problem simultaneously, a ..."
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Cited by 4 (3 self)
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Logical inference is one approach to implementing the reasoning component of a cognitive system. Inference graphs are a method for natural deduction inference which, uniquely in logicbased cognitive systems, use concurrency to reason about multiple possible ways to solve a problem simultaneously, and cancel nolongernecessary inference operations. We outline extensions to inference graphs which increase their usefulness in cognitive systems, including: the use of a more expressive logic; a method for “wh question ” answering; and a way to focus reasoning on problems which cannot immediately be answered due to incomplete information, so when more information becomes available the inference can proceed. We discuss how these three improvements increase the usefulness of inference graphs in cognitive systems.
Meinongian Semantics and Artificial Intelligence
, 2012
"... This essay describes computational semantic networks for a philosophical audience and surveys several approaches to semanticnetwork semantics. In particular, propositional semantic networks (exemplified by SNePS) are discussed; it is argued that only a fully intensional, Meinongian semantics is appr ..."
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This essay describes computational semantic networks for a philosophical audience and surveys several approaches to semanticnetwork semantics. In particular, propositional semantic networks (exemplified by SNePS) are discussed; it is argued that only a fully intensional, Meinongian semantics is appropriate for them; and several Meinongian systems are presented. 1 1 Meinong, Philosophy, and Artificial Intelligence Philosophy was not kind to Meinong, the late19th/early20thcentury cognitive scientist, until the 1970s renaissance in Meinong studies (Findlay
ANALOG: A Logical Language for Natural Language Processing
, 1994
"... We present a formal description of a logical language that is based on a propositional semantic network. Variables in this language are not atomic and have potentially complex structure. We start from the individual components of a semantic network system, atomic nodes and relations that connect nod ..."
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We present a formal description of a logical language that is based on a propositional semantic network. Variables in this language are not atomic and have potentially complex structure. We start from the individual components of a semantic network system, atomic nodes and relations that connect nodes, and provide a complete specification for the structure of nodes and a subsumption procedure between nodes. We differ from other work in subsumption in that the representation language is uniform and based on an extended firstorder predicate logic. The language is particularly suitable for addressing some problems associated with natural language processing, namely the representation of complex natural language descriptions and inference associated with description subsumption.