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 chapter presents several aspects of such a logic.

We have been engaged in a series of projects in which Cassie, the SNePS cognitive agent, has been incorporated into a hardware- or software-simulated cognitive robot. In this paper, we present an informal summary of our approach to anchoring the abstract symbolic terms that denote Cassie's mental entities in the lowerlevel structures used by embodied-Cassie to operate in the real (or simulated) world. We discuss anchoring in the domains of: perceivable entities and properties, actions, time, and language.

As part of an interdisciplinary project to develop a computational cognitive model of a reader of narrative text, we are developing a computational theory of how natural-language-understanding systems can automatically acquire new vocabulary by determining from context the meaning of words that are unknown, misunderstood, or used in a new sense. `Context' includes surrounding text, grammatical information, and background knowledge, but no external sources. Our thesis is that the meaning of such a word can be determined from context, can be revised upon further encounters with the word, "converges" to a dictionary-like definition if enough context has been provided and there have been enough exposures to the word, and eventually "settles down" to a "steady state" that is always subject to revision upon further encounters with the word. The system is being implemented in the SNePS knowledgerepresentation and reasoning system. This essay is forthcoming as a chapter in Iwanska, L/ucja, & S...

Natural language competent embodied cognitive agents should satisfy two requirements. First, they should act in and reason about a changing world, using reasoning in the service of acting and acting in the service of reasoning. Second, they should be able to communicate their beliefs, and report their past, ongoing, and future actions in natural language. This requires a representation of time using a deictic NOW, that models the compositional semantic properties of the English "now". Two problems emerge for an agent that interleaves reasoning and acting in a personal time. The first concerns the representation of plans and reactive rules involving reasoning about "future NOWs". The second emerges when, in the course of reasoning about NOW, the reasoning process itself results in NOW changing. We propose solutions for the two problems and conclude that: (i) for embodied cognitive agents, time is not just the object of reasoning, but is embedded in the reasoning pr...

As part of an interdisciplinary project to develop a computational cognitive model of a reader of narrative text, we are developing a computational theory of how natural-languageunderstanding systems can automatically expand their vocabulary by determining from context the meaning of words that are unknown, misunderstood, or used in a new sense. `Context ' includes surrounding text, grammatical information, and background knowledge, but no external sources. Our thesis is that the meaning of such a word can be determined from context, can be revised upon further encounters with the word, "converges" to a dictionary-like definition if enough context has been provided and there have been enough exposures to the word, and eventually "settles down" to a "steady state" that is always subject to revision upon further encounters with the word. The system is being implemented in the SNePS knowledge-representation and reasoning system. This document is a slightly modified version (containing the...

The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Testfpassing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will inevitably be persons.

Contextual vocabulary acquisition (CVA) is the active, deliberate acquisition of a meaning for an unknown word in a text by reasoning from textual clues, prior knowledge, and hypotheses developed from prior encounters with the word, but without external sources of help such as dictionaries or people. Published strategies for doing CVA vaguely and unhelpfully tell the reader to “guess”. AI algorithms for CVA can fill in the details that replace “guessing ” by “computing”; these details can then be converted to a curriculum that can be taught to students to improve their reading comprehension. Such algorithms also suggest a way out of the Chinese Room and show how holistic semantics can withstand certain objections. 1 1 Computational Philosophy and Philosophical Computation Computer science in general, and AI in particular, have a lot to give to philosophy, and vice versa, as Daniel Dennett once noted (1978: 126; cf. Rapaport 1986b). This essay discusses an interdisciplinary, applied cognitive-science research project that exhibits how philosophy can influence AI, how AI can influence philosophy, and how both can influence educational practice. I 1 take “computational philosophy ” to be the application of computational (i.e., algorithmic) solutions to philosophical problems. 2 An example from my own research would be the use of the SNePS knowledgerepresentation,

of an embodied computational cognitive agent called Cassie, based on the Grounded Layered Architecture with Integrated Reasoning (GLAIR). In this document we describe a new implementation, in which Cassie's body and the world are simulated in Crystal Space, an environment for building 3-D games. We describe the implementation of Cassie in a Crystal Space environment including her current suite of actions and her simulated vision system. Crystal Cassie is a tool for cognitive modeling and testing cognitive theories.

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SNePS, the Semantic Network Processing System, has been designed to be a system for representing the beliefs of a natural-language-using intelligent system (a "cognitive agent"). This paper expands on this motivation, discusses some of the system features that derived from this motivation, and presents four case studies of interactions with SNePS demonstrating some of these features. The features demonstrated in the case studies are: nonstandard connectives; the use of recursive rules; the Unique Variable Binding Rule, that says that two variables in a rule cannot be instantiated to the same term; and discussing sentences and propositions in natural language. 1 System Description SNePS, the Semantic Network Processing System [9, 15, 17], has been designed to be a system for representing the beliefs of a natural-language-using intelligent system (a "cognitive agent"). It has always been the intention that a SNePSbased "knowledge base" would ultimately be built, not by a programmer or k...