Cyc

Lenat, Douglas B.

doi:10.1145/219717.219745

Cited by 1,220 publications

(455 citation statements)

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“…Our arguments in favor of structured models build on claims made by previous researchers (Geman et al, 1992;Chomsky, 1986), and psychologists and AI researchers have been developing structured models for many years (Collins & Quillian, 1969;Davis, 1990;Lenat, 1995).…”

Section: Modeling Frameworkmentioning

confidence: 84%

“…First, it explains human judgments as statistical inferences, and second, it uses different kinds of structures to capture different kinds of background knowledge. Our approach therefore combines the insights of two groups of researchers: those that emphasize structured knowledge but not statistical inference (Collins & Quillian, 1969;Chomsky, 1986;Lenat, 1995), and those that emphasize statistics but not structure (Rogers & McClelland, 2004). …”

Section: Discussionmentioning

confidence: 99%

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Structured statistical models of inductive reasoning.

Kemp¹,

Tenenbaum²

2009

Psychological Review

222

282

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Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge, and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. We present a Bayesian framework that attempts to meet both goals and describe four applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the four models are defined over different kinds of structures that capture different relationships between the categories in a domain. Our framework therefore shows how statistical inference can operate over structured background knowledge, and we argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning. 2 Structured statistical models of inductive reasoningHumans are adept at making inferences that take them beyond the limits of their direct experience. Even young children can learn the meaning of a novel word from a single labeled example (Heibeck & Markman, 1987), predict the trajectory of a moving object when it passes behind an occluder (Spelke, 1990), and choose a gait that allows them to walk over terrain they have never before encountered. Inferences like these may differ in many respects, but common to them all is the need to "go beyond the information given" (Bruner, 1973).Two different ways of going beyond the available information can be distinguished.Deductive inferences draw out conclusions that may have been previously unstated but were implicit in the data provided. Inductive inferences go beyond the available data in a more fundamental way, and arrive at conclusions that are likely but not certain given the available evidence. Both kinds of inferences are of psychological interest, but inductive inferences appear to play a more central role in everyday cognition. We have already seen examples related to language, vision, and motor control, and many other inductive problems have been described in the literature (Holland, Holyoak, Nisbett, & Thagard, 1986;Anderson, 1990). This paper describes a formal approach to inductive inference that should apply to many different problems, but we will focus on the problem of property induction (Sloman & Lagnado, 2005). In particular, we consider cases where one or more more categories in a domain are observed to have a novel property, and the inductive task is to predict how the property is distributed over the remaining categories in the domain. For instance, given that bears have sesamoid bones, which species is more likely to share this property: moose or salmon (Rips, 1975; Osherson, Smith, Wilkie, Lopez, & Shafir, 1990)? Moose may seem like the better choice since they are more similar biologically to bears, but different properties can lead to different patterns of inference. For example, given that a c...

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Section: Modeling Frameworkmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Structured statistical models of inductive reasoning.

Kemp¹,

Tenenbaum²

2009

Psychological Review

222

282

View full text Add to dashboard Cite

show abstract

“…but no specialized "domain" knowledge. We include under this rubric both the sort of very basic commonsense information that the CYC knowledge-representation and reasoning system is concerned with (Lenat 1995) 15 and the somewhat more domain-specific information that the "cultural literacy" movement is concerned with (Hirsch 1987(Hirsch , 2003.…”

Section: Priormentioning

confidence: 99%

Contextual Vocabulary Acquisition: from Algorithm to Curriculum

Rapaport¹,

Kibby²

2014

Castañeda and His Guises

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Deliberate contextual vocabulary acquisition (CVA) is a reader's ability to figure out a (not the) meaning for an unknown word from its "context", without external sources of help such as dictionaries or people. The appropriate context for such CVA is the "belief-revised integration" of the reader's prior knowledge with the reader's "internalization" of the text. We discuss unwarranted assumptions behind some classic objections to CVA, and present and defend a computational theory of CVA that we have adapted to a new classroom curriculum designed to help students use CVA to improve their reading comprehension. Running head: Contextual Vocabulary Acquisition Keywords:Artificial Intelligence, Education, Linguistics, Philosophy; Instruction, Language acquisition, Language understanding, Learning, Reasoning, Semantics; Knowledge representation, Logic, Symbolic computational modeling.2 From Guise Theory to Contextual Vocabulary Acquisition An Opening AnecdoteWhen Hector-Neri Castañeda was ill, Randall R. Dipert and I (WJR) visited him in Bloomington. He was weak and tired, and his speech was even harder to decipher than it had been when he was healthy. He was happy to see us, however, and, after a few preliminaries, suggested that we have dinner later at his favorite macrobiotic restaurant.The restaurant was exceedingly noisy, rendering dinner conversation virtually impossible. Although he was tired after dinner, he insisted that we come back to his house to talk philosophy. And, as soon as we began our philosophical conversation, his stamina returned, his voice became stronger and clearer, and we were back with the Hector of old. Philosophy gave him strength. Castañeda's Guise Theory.Guise theory (Castañeda 1972(Castañeda , 1999, and many works in between; for a summary, see Rapaport 2005b) has two goals: to serve as a theory of the mechanism of reference and to serve as a theory of the world as it appears to us.And it has two principal sources: Frege's (1892) puzzle of reference and the "univocality" of language. Frege's puzzle concerns the way in which the President of the US is the "same" as the Commander-in-Chief of the US Armed Forces. The univocality of language is the claim that talk and thought about truth and reality behaves exactly the same as talk and thought about falsehood and fiction: Language treats objects of both kinds of talk and thought in the same way. This is associated with a principle of "metaphysical internalism": Talk and thought about the world is internal to experience; the (experienced) world must be understood from the "inside", not "externally"-from a first-person point of view, not from God's point of view or Nagel's (1986) "view from nowhere".Metaphysical internalism has proven useful in artificial intelligence (AI) and cognitive modeling (Shapiro & Rapaport 1987.According to guise theory, there are properties, sets of properties (called "guise cores"), and an "individuating operator" (c) that maps guise cores to guises. E.g., where Round is the property of being round and Squ...

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“…We believe that in domains that deal with engineered physical systems, many of the domain axiomatizations have these structural properties. Indeed, design of engineering artifacts encourages modularization, with minimal interconnectivity (see [1,28,13]). More generally, we believe axiomatizers of large corpora of real-world knowledge tend to try to provide structured representations following some of these principles.…”

Section: A Good Partitioningmentioning

confidence: 99%

Improving the Efficiency of Reasoning Through Structure-Based Reformulation

Amir

McIlraith

2000

Lecture Notes in Computer Science

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Abstract.We investigate the possibility of improving the efficiency of reasoning through structure-based partitioning of logical theories, combined with partitionbased logical reasoning strategies. To this end, we provide algorithms for reasoning with partitions of axioms in first-order and propositional logic. We analyze the computational benefit of our algorithms and detect those parameters of a partitioning that influence the efficiency of computation. These parameters are the number of symbols shared by a pair of partitions, the size of each partition, and the topology of the partitioning. Finally, we provide a greedy algorithm that automatically reformulates a given theory into partitions, exploiting the parameters that influence the efficiency of computation.

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Cyc

Cited by 1,220 publications

References 4 publications

Structured statistical models of inductive reasoning.

Structured statistical models of inductive reasoning.

Contextual Vocabulary Acquisition: from Algorithm to Curriculum

Improving the Efficiency of Reasoning Through Structure-Based Reformulation

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