Parallel Processing for Artificial Intelligence (Machine by C.B. Suttner

By C.B. Suttner

This can be the 3rd quantity in a casual sequence of books approximately parallel processing for synthetic intelligence. it's in response to the belief that the computational calls for of many AI projects should be higher served by way of parallel architectures than by means of the at present well known workstations. notwithstanding, no assumption is made concerning the form of parallelism for use. Transputers, connection machines, farms of workstations, mobile neural networks, crays and different paradigms of parallelism are utilized by the authors of this assortment. The papers come up from the parts of parallel wisdom illustration, neural modeling, parallel non-monotonic reasoning, seek and partitioning, constraint pride, theorem proving, parallel choice timber, parallel programming languages and low-level laptop imaginative and prescient. the ultimate paper is a record approximately purposes of huge parallelism and goals to trap the spirit of an entire interval of computing heritage.

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Metaphors We Live By. University of Chicago Press, Chicago, 1980. 14. D. R. Mani. The Design and Implementation of Massively Parallel Knowledge Representation and Reasoning Systems: A Connectionist Approach. PhD thesis, Depart- 39 ment of Computer and Information Science, University of Pennsylvania, 1995. 15. D. R. Mani and L. Shastri. Reflexive reasoning with multiple instantiation in a connectionist reasoning system with a type hierarchy. Connection Science, 5(3 & 4):205-242, 1993. 16. G. A. Miller, R.

2. W E I G H T E D INHERITANCE NETWORKS The underlying structure of a Weighted Inheritance Network is a directed acyclic graph. Knowledge is represented by attaching to each node of the graph a label that denotes an object, a class of objects or a property possessed by objects and by establishing the desired inheritance structure through the insertion of properly directed edges. When we insert an edge that emanates from a node and is directed to another, we mean that the class of objects represented by the former node inherits some or all of the defining properties of the class represented by the latter.

Fellbaum, D. Gross, K. Miller, and R. Tengi. Five papers on WordNet. Technical Report CSL-43, Princeton University, July 1990. Revised March 1993. 17. L. Shastri. Massive parallelism in artificial intelligence. Technical Report MS-CIS86-77, University of Pennsylvania, 1986. 18. L. Shastri. Semantic Networks: An Evidential Formulation and its Connectionist Realization. Morgan Kaufmann, San Mateo, CA, 1988. 19. L. Shastri. Default reasoning in semantic networks: A formalization of recognition and inheritance.

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