Psychological investigations have led to considerable insight into the working of the human language comprehension system. In this article, we look at a set of principles derived from psychological findings to argue for a particular organization of linguistic knowledge along with a particular processing strategy and present a computational model of sentence processing based on those principles. Many studies have shown that human sentence comprehension is an incremental and interactive process in which semantic and other higher-level information interacts with syntactic information to make informed commitments as early as possible at a local ambiguity. Early commitments may be made by using top-down guidance from knowledge of different types, each of which must be applicable independently of others. Further evidence from studies of error recovery and delayed decisions points toward an arbitration mechanism for combining syntactic and semantic information in resolving ambiguities. In order to account for all of the above, we propose that all types of linguistic knowledge must be represented in a common form but must be separable so that they can be applied independently of each other and integrated at processing time by the arbitrator. W e present such a uniform representation and a computational model called COMPERE based on the representation and the processing strategy.

The development of models of human sentence processing hais traditionally followed one of two paths. Either the model posited a sequence of processing modules, each with its own taskspecific knowledge (e.g., syntax and semantics), or it posited a single processor utilizing different types of knowledge inextricably integrated into a monolithic knowledge base. Our previous work in modeling the sentence processor resulted in a model in which different processing modules used separate knowledge sources but operated in paral el to arrive at the interpretation of a sentence. One highlight of this model is that it offered an explanation of how the sentence processor might recover from an error in choosing the meaning of an ambiguous word: the semantic processor briefly pursued the different interpretations associated with the different meanings of the word in question until additional text confirmed one of them, or until processing limitations were exceeded. Errors in syntactic ambiguity resolution were assumed to be handled in some other way by a separate syntactic module. Recent experimental work by Laurie Stowe strongly suggests that the human sentence processor deals with syntactic error recovery using a mechanism very much like that proposed by our model of semantic error recovery. Another way to interpret Stowe's finding that two significantly different kinds of errors are handled in the same way is this: the human sentence processor consists of a single unified processing module utilizing multiple independent knowledge sources in parallel. A sentence processor built upon this architecture should at times exhibit behavior eissociated with modular approaches, and at other times act like an integrated system. In this paper we explore some of these ideas via a prototype computational model of sentence processing ca led C O M P E R E , and propose a set of psychological experiments for testing our theories.

Cognitive agents often acquire knowledge of how devices work by reading a book. We describe a computational theory of understanding a natural language description of a device, comprehending how the device works, and acquiring a device model. The theory posits a complex interplay between language, memory, comprehension, problem-solving and learning faculties. Long-term memory contains cases of previously encountered devices and associated structure-behavior-function (SBF) models that explain how the known device works. Language processing is both bottom-up and top-down. Bottom-up processing is done through spreading-activation networks. Where the semantics of the nodes and links in the network arises from the SBF ontology. The comprehension process constructs a SBF model for the new device by adapting the known device models - we call this process adaptive modeling. This multifaculty computational theory is instantiated in an operational computer system called KA that (i) reads and understands English language descriptions of devices from David Macaulay's popular science book The Way Things Work, (ii) comprehends how the described device works, and (iii) acquires a SBF model for the device.

In this paper, we investigate the interaction between linguistic and ncn-linguistic processes by considering the role of functional reasoning in understanding design specifications written in natural language. W e desaibe KA , an experimental modelbased interpretation and design system which understands English language descriptions of the design problems it solves, and examine whether KA's problem-solving amiabilities help i) ascertain the relevance of ambiguous design ^lecifications and ii) identify unspecified relations between design requirements. Our results demonstrate that augmenting language processing with the ability to reason about function along the lines suggested in K A provides effective solutions to these problems in particular as well as to other problems in natural language understanding.

Traditional Cognitive Science has studied various cognitive components in isolation. Our project at- tempts to alleviate some of the problems with this separation by focusing on the role of problem solv- ing in language comprehension. Specifically, the KA project integrates six areas of current investi- gation in Cognitive Science: knowledge represen- tation, m e m o r y organization, language compre- hension, knowledge acquisition, problem solving, and control architectures. W e are developing a model-based text interpretation and knowledge ac- quisition system which, when completed, will be able to read and interpret descriptions of physical devices, construct models of the devices, and use the acquired models to solve novel design prob- lems. This paper presents three areas in which we use problem solving to constrain natural language understanding: (1) the use of mental models as a foundation for both problem solving and natu- ral language understanding, (2) the use of design experience to influence the understanding process, and (3) the use of the design process to establish the cost of linguistic decisions.