Technology
Modelling Distributed Systems
Distributed systems are multi-processor information processing systems whichdo not rely on the central shared memory for communication. The importanceof distributed systems has been growing with the advent of "computer networks"of a wide spectrum: networks of geographically distributed computers at one end,and tightly coupled systems built with a large number of inexpensive physicalprocessors at the other end. Both kinds of distributed system are made availableby the rapid progress in the technology of large-scale integrated circuits. Yetlittle has been done in the research on semantics and programming methodologiesfor distributed information processing systems.Our main research goal is to understand and describe the behaviour of suchdistributed systems in seeking the maximum benefit of employing multi-processorcomputation schemata.Hayes, J.E., D. Michie, and L. I. Mikulich (Eds.), Machine Intelligence 9, Ellis Horwood.
On the epistemological status of semantic networks
This paper examines in detail the history of a set of network-structured formalisms for knowledge representation - the so-called semantic networks. While these nets have for the most part retained their basic associative nature, their primitive representational elements have differed significantly from one project to the next. These differences in underlying primitives are symptomatic of deeper philosophical disparities, and a set of five significantly different levels at which networks can be understood are discussed. One of these levels, the epistemological, or knowledge-structuring, level, has played an important implicit part in all previous notations, and is here made explicit in a way that allows a new type of network formalism to be specified. This new type of formalism accounts precisely for operations like individuation of description, internal concept structure in terms of roles and interrelations between them, and structured inheritance.
Semantic network representations in rule-based inference systems
Duda, R. O. | Hart, P. E. | Nilsson, N. J. | Sutherland, G. L.
"Rule-based inference systems allow judgmental knowledge about a specific problem domain to be represented as a collection of discrete rules. Each rule states that if certain premises are known, then certain conclusions can be inferred. An important design issue concerns the representational form for the premises and conclusions of the rules. We describe a rule-based system that uses a partitioned semantic network representation for the premises and conclusions." In D. A. Waterman and Frederick Hayes-Roth. 1978. Pattern-Directed Inference Systems. Academic Press, Inc., Orlando, FL, USA. pp. 203-221.