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In this paper and attached video, we present a third-generation expert system named Knowledge Amplification by Structured Expert Randomization (KASER) for which a patent has been filed by the U.S. Navy's SPAWAR Systems Center, San Diego, CA (SSC SD). KASER is a creative expert system. It is capable of deductive, inductive, and mixed derivations. Its qualitative creativity is realized by using a tree-search mechanism. The system achieves creative reasoning by using a declarative representation of knowledge consisting of object trees and inheritance. KASER computes with words and phrases. It possesses a capability for metaphor-based explanations. This capability is useful in explaining its creative suggestions and serves to augment the capabilities provided by the explanation subsystems of conventional expert systems. KASER also exhibits an accelerated capability to learn. However, this capability depends on the particulars of the selected application domain. For example, application domains such as the game of chess exhibit a high degree of geometric symmetry. Conversely, application domains such as the game of craps played with two dice exhibit no predictable pattern, unless the dice are loaded. More generally, we say that domains whose informative content can be compressed to a significant degree without loss (or with relatively little loss) are symmetric. Incompressible domains are said to be asymmetric or random. The measure of symmetry pins the measure of randomness must always sum to unity. Index Terms--Expert systems, knowledge amplification, machine learning, object-oriented design, randomization.

The number of information sources and the volumes of data in these information sources have greatly increased, which may be attributed to the ever-increasing complexity of real-world applications. The enormous amount of information available in the information sources in a distributed information-providing environment has created a need to provide users with tools to effectively and efficiently navigate and retrieve information. Queries in such an environment often access information from multiple information sources. This may be attributed to navigational characteristics. Clusters provide a structure for organizing the large number of information sources for efficient browsing, searching, and retrieval. This paper presents a stochastically-based clustering mechanism, called the Markov model mediator (MMM), to group the information sources into a set of useful clusters. Each information source cluster groups those information sources that show similarities in their data access behavior. Information sources within the same cluster are expected to be able to provide most of the required information among themselves for user queries that are closely related with respect to a particular application. This can significantly improve system response time, query performance, and result in an overall improvement in decision support. Empirical studies on real databases are performed and the results demonstrate that our proposed mechanism leads to a better set of clusters in comparison with other clustering methods. This serves to illustrate the effectiveness of our proposed MMM mechanism. Index Terms--Distributed information sources, Markov model mediator (MMM), stochastic clustering.

Computing with words is defined, in this paper, to be a symbolic generalization of fuzzy logic, which admits self-reference. It entails the randomization of declarative knowledge, which yields procedural knowledge. Such randomization can occur at two levels. First is termed weak randomization, which is essentially a domain-general pattern-matching operation. Second is termed strong randomization, which entails the application of one rule set to the semantics of another - possibly including itself. Strong randomization rests on top of weak randomization. Strong randomization is essentially a heuristic process. It is fully scalable, since it can in theory map out its own needed heuristics for evermore efficient search - including segmentation of the knowledge base. It is proven that strong learning must be knowledge-based, if effective. Computing with words does not preclude the use of predicate functions or procedural attachments. Also, the paradigm for computing with words does not directly compete with that for fuzzy logic. Rather, it serves to augment the utility of fuzzy logic through symbolic randomization. A countably infinite number of domain-specific logics or knowledge-based methods for randomization exist. Index Terms - Fuzzy logic, intelligent systems, knowledge acquisition, randomization.