Abstract not found

this paper. In particular, the protocols we have collected often involve picturing different situations (e.g., a mental map of South America, images of savannas, or an advertisement showing Juan Valdez on his coffee plantation in Colombia). These im- ages can be taken as evidence for the manipulation of mental models in Johnson-Laird's terms. But overlaying this manipulation of mental models are the systematic patterns in which they are deployed to support one's con- clusions (cf. Rips, 1986). So while mental models may be part of the story of plausible reasoning, there is another critical part which the theory we pro- pose addresses. The theory does not address the issue of whether people make systematic errors in their reasoning, as the psychological literature on decision making (Kahneman, Slovic, & Tversky, 1982) attempts to document. This issue does not arise in the theory because we are developing a formalism for representing the kinds of inferences people make and the parameters that affect their certainty, rather than a theory about how people make particular inferences. People may systematically ignore some kinds of information or undervalue particular certainty parameters--we have not attempted to determine whether they do or not. Instead we have tried to represent all the kinds of reasoning patterns and the kinds of certainty parameters that appear in the protocols we have analyzed (Collins, 1978a, 1978b). In this regard it is worth pointing out that certain fallacles in logic, such as affirming the consequent (Havi- land, 1974), become plausible inference patterns in the theory.' The theory was developed to account for protocols where. a question drives the search fo relevant information; in Artificial Intelligence this is called backward inferencing. One qu...

Officer praised dog for barking at object." Enables Detect Drugs out FK Initiates Retrieval 5 6 Missing Figure 10. Forgetting to fill the tank with gas A=actual intention; E=expectation; Q=question; C=context; I=index; G=goal Tank Out of Gas Tank Full Tank Low Fill Tank Should have filled up with gas when tank low Expectation What Action to Do? KEY: G = goal; I = index; C = context; Q = question; E = expectation; A = actual intention Results At Store connections with related concepts. Other learning goals take multiple arguments. For instance, a knowledge differentiation goal (Cox & Ram, 1995) is a goal to determine a change in a body of knowledge such that two items are separated conceptually. In contrast, a knowledge reconciliation goal (Cox & Ram, 1995) is one that seeks to merge two items that were mistakenly considered separate entities. Both expansion goals and reconciliation goals may include or spawn a knowledge organization goal (Ram, 1993) that seeks to reorganize the existing knowledge so that it is made available to the reasoner at the appropriate time, as well as modify the structure or content of a concept itself. Such reorganization of knowledge affects the conditions under which a particular piece of knowledge is retrieved or the kinds of indexes associated with an item in memory.

This paper presents an approach to natural language grammars and parsing in which slots and rules for filling them play a major role. The system described provides a natural way of handling a wide variety of grammatical phenomena, such as WH- movement, verb dependencies, and agreement

In traditional knowledge-based system development environments, the fundamental representational building blocks are mechanisms such as frames, rules, and attached procedures. The KEE system has been extended to include both a context (worlds) system and a truth maintenance system.

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Traditional computer simulation terminology includes taxonomic divisions with terms such as "discrete event," "continuous," and "process oriented." Even though such terms have become familiar to simulation researchers, the terminology is distinct from other disciplines ---such as artificial intelligence and software engineering--- which have similar goals relating specifically to modelling dynamic systems. There is a need to unify terminology among these disciplines so that system modelling is formalized in a common framework. We present a perspective that serves to characterize simulation models in terms of their procedural versus declarative orientations since these two orientations are prevalent throughout most modelling disciplines that we have encountered. We used a sample dynamic system (e.g., two jug problem) found in artificial intelligence to highlight the connecting threads in system modelling within each discipline. Moreover, in teaching simulation students using this perspe...

Abstract-Schema-based representations for visual knowledge are integrated with constraint satisfaction techniques. This integration is discussed in a progression of three sketch map interpretation pro-grams: Mapsee-1, Mapsee-2, and Mapsee-3. The programs are evalu-ated by the criteria of descriptive and procedural adequacy. The eval-uation indicates that a schema-based representation used in combination with a hierarchical arc consistency algorithm constitutes a modular, efficient, and effective approach to the structured represen-tation of visual knowledge. The schemata used in this representation are embedded in composition and specialization hierarchies. Speciali-zation hierarchies are further expanded into discrimination graphs. Index Terms-Constraint satisfaction, discrimination graphs, hier-archical arc consistency, model-based vision, recognition, schema rep-resentations, sketch maps. I.

This paper concerns a knowledge structure called method , within a computational model for human oriented deduction. With human oriented theorem proving cast as an interleaving process of planning and verification, the body of all methods reflects the reasoning repertoire of a reasoning system. While we adopt the general structure of methods introduced by Alan Bundy, we make an essential advancement in that we strictly separate the declarative knowledge from the procedural knowledge. This is achieved by postulating some standard types of knowledge we have identified, such as inference rules, assertions, and proof schemata, together with corresponding knowledge interpreters. Our approach in effect changes the way deductive knowledge is encoded: A new compound declarative knowledge structure, the proof schema, takes the place of complicated procedures for modeling specific proof strategies. This change of paradigm not only leads to representations easier to understand, it also enables us...

The emergence of expert system methodology as the cutting edge of the application of artificial intelligence techniques has tended to refocus the issues of the nature of intelligence. Expert systems are clearly not intelligent in the meaning espoused by early workers in AI. Indeed, they are intended to be transparent in operation to the user. However, they are competent in their domain of application. This notion of competence and of a competence model turns out to be an appropriate conceptual analysis for much of the early efforts in AI, as well as current work in the field.