ISSN:0954-0091    

DOI:10.1080/09540098908915639

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

Machine learning is an area where both symbolic and neural approaches to artificial intelligence have been heavily investigated. However, there has been little research into the synergies achievable by combining these two learning paradigms. A hybrid system that combines the symbolically-oriented explanation-based learning paradigm with the neural backpropagation algorithm is described. In the presented EBL-ANN algorithm, the initial neural network configuration is determined by the generalized explanation of the solution to a specific classification task. This approach overcomes problems that arise when using imperfect theories to build explanations and addresses the problem of choosing a good initial neural network configuration. Empirical results show that the hybrid system more accurately learns a concept than the explanation-based system by itself and learns faster and generalizes better than the neural learning system by itself.

ISSN:0954-0091    

DOI:10.1080/09540098908915640

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

This paper describes a hybrid model which integrates symbolic and connectionist techniques for the analysis of noun phrases. Our model consists of three levels: (1) a distributed connectionist level, (2) a localist connectionist level, and (3) a symbolic level. While most current systems in natural language processing use techniques from only one of these three levels, our model takes advantage of the virtues of all three processing paradigms. The distributed connectionist level provides a learned semantic memory model. The localist connectionist level integrates semantic and syntactic constraints. The symbolic level is responsible for restricted syntactic analysis and concept extraction. We conclude that a hybrid model is potentially stronger than models that rely on only one processing paradigm.

ISSN:0954-0091    

DOI:10.1080/09540098908915641

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

A new cognitive architecture is proposed for the syntactic aspects of human sentence processing. The architecture, called Unification Space, is biologically inspired but not based on neural nets. Instead it relies on biosynthesis as a basic metaphor. We use simulated annealing as an optimization technique which searches for the best configuration of isolated syntactic segments or subtrees in the final parse tree. The gradually decaying activation of individual syntactic nodes determines the ‘global excitation level’ of the system. This parameter serves the function of ‘computational temperature’ in simulated annealing. We have built a computer implementation of the architecture which simulates well-known sentence understanding phenomena. We report successful simulations of the psycholinguistic effects of clause embedding, minimal attachment, right association and lexical ambiguity. In addition, we simulated impaired sentence understanding as observable in agrammatic patients. Since the Unification Space allows for contextual (semantic and pragmatic) influences on the syntactic tree formation process, it belongs to the class of interactive sentence processing models.

ISSN:0954-0091    

DOI:10.1080/09540098908915642

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

This paper presents a novel approach to the problem of action selection for an autonomous agent. An agent is viewed as a collection of competence modules. Action selection is modelled as an emergent property of an activation/inhibition dynamics among these modules. A concrete action selection algorithm is presented and a detailed account of the results is given. This algorithm combines characteristics of both traditional planners and reactive systems. It provides global parameters, which one can use to tune the action selection behavior along several criteria, such as goal orientedness versus situation orientedness, bias towards ongoing plans versus adaptivity, and sensitivity to goal conflicts and ‘thoughtfulness’ versus speed.

ISSN:0954-0091    

DOI:10.1080/09540098908915643

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

The rule base and the inference engine of a knowledge-based system are transformed into a kind of neural network called a conceptualization network. An approach is presented that generalizes the backpropagation teaming rule of the neural-network approach such that it can effectively deal with errors in conceptualization networks, which are often multilayered and involve logic conjunction. The idea is to use hill-climbing search where the backpropagation rule falls short because the transfer function is not differentiable. When the generalized backpropagation rule is applied to a conceptualization network which has been constrained by initial correct knowledge, incorrect rules can be recognized. Experiments in a practical domain have demonstrated that the approach can satisfactorily conduct credit and blame assignment for rules which may involve intermediate concepts and logic conjunction. Effective removal of incorrect rules with significant improvement of the system performance has been observed.

ISSN:0954-0091    

DOI:10.1080/09540098908915644

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

ISSN:0954-0091    

DOI:10.1080/09540098908915645

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor