Semantics can provide the basis for problem solving in a setting such as telecommunications network management, which maintains multiple simultaneous ontologies for the same set of entities. To avoid overbuilding and to improve quality, network operators desire both a close match between application requirements and allocated resources as well as the capability to perform modifications at will. This paper presents a concept for solving the relevant problem of network resource allocation through semantic reasoning techniques and identifies three specific domains for development of common ontologies

The authors are developing a generic control architecture suitable for use as a single intelligent agent or as multiple cooperating agents. The generic architecture combines a task-oriented domain controller with a meta-controller that schedules activities within the domain controller. The domain controller provides functions for model-based situation assessment and planning, and inter-controller communication. Typically, these functions are performed by modules taken from a repository of reusable software. To improve the controller development process, the authors are combining many of the best ideas from software engineering and knowledge engineering in a software environment. This environment includes a blackboard-like development workspace to represent both the software under development and the software development process itself.>

The author suggests a new focus on benchmarks for knowledge systems, following the lines of similar benchmarks in other computing fields. It is noted that knowledge systems differ from conventional systems in a key way, namely their ability to interpret and apply knowledge. This gives rise to a distinction between intrinsic measures concerned with engineering qualities and extrinsic measures relating to task productivity, and both warrant improved measurement techniques. Primary concerns within the extrinsic realm include advice quality, reasoning correctness, robustness, and solution efficiency. Intrinsic concerns, on the other hand, center on elegance of knowledge base design, modularity, and architecture. The author suggests criteria for good measures and benchmarks, and ways to satisfy these through the design of knowledge and key knowledge engineering costs and performance parameters. It is suggest that the focus on measuring knowledge systems should help clarify the technical relationships between knowledge engineering and data engineering.>

The ABE multilevel architecture for developing intelligent systems addresses the key problems of intelligent systems engineering: large-scale applications and the reuse and integration of software components. ABE defines a virtual machine for module-oriented programming and a cooperative operating system that provides access to the capabilities of that virtual machine. On top of the virtual machine, ABE provides a number of system design and development frameworks, which embody such programming metaphors as control flow, blackboards, and dataflow. These frameworks support the construction of capabilities, including knowledge processing tools, which span a range from primitive modules to skeletal systems. Finally, applications can be built on skeletal systems. In addition, ABE supports the importation of existing software, including both conventional and knowledge processing tools.>

A new approach to situation assessment is an automated distributed sensor network (DSN) consisting of many "intelligent" sensor devices that can pool their knowledge to achieve an accurate overall assessment of a situation. Laboratory experiments were conducted to investigate potential DSN organizations and to ascertain some general design principles. These experiments have been performed with a network of "sensor nodes," each of whom sees only a small portion of the entire environment and attempts to identify the environmental mobile entities as quickly as possible. To do this, they must cooperatively communicate their hypotheses and data, using a limited number of messages. Two general DSN organizations were tested. The first was hierarchical. The second was an "anarchic committee" whose nodes could each send messages to one, some, or all other nodes. The performance of the committee organization consistently surpassed the hierarchical one. This lent support to the contention that DSN architectures need to emphasize cooperative aspects of problem-solving. A machine-based simulation of such a network that achieved performance levels comparable to that of the human committee DSN organization was also constructed and tested. Because most situation assessment communications concern hypothesis updating and revision, minimizing communication requirements through the concept of active "hypothesis processes," which are responsible for predicting their own evolution over time, is suggested.

The ability of a set of simple predicates to capture characteristic patterns in a parametric representation of vowels in continuous speech was investigated with the aid of an efficient conjunctive pattern recognition and classification system. The results compare favourably with those produced by a cluster-based minimal Euclidean distance technique, run over the identical training and test samples. The predicates used are similar to auditory receptive fields.

The Hearsay II speech understanding system under development at Carnegie-Mellon University is a complex, distributed-logic processing system: Processing in the system is affected by independent, data-directed knowledge sources processes which examine and alter values in a global data base representing hypothesized phones, phonemes, syllables, words, and phrases, as well as the hypothetical temporal and logical relationships among them. The question of how to schedule the numerous potential activities of the knowledge sources so as to understand the utterance in minimal time is called the "focus of attention problem". Near optimal focusing is especially important in a speech understanding system because of the very large solution space that potentially needs to be searched. Using the concepts of stimulus and response frames of scheduled knowledge source instantiations, competition among alternative responses, goals, and the desirability of a knowledge source instantiation, a general attentional control mechanism is developed. This general focusing mechanism facilitates the experimental evaluation of a variety of specific attentional control policies (such as best-first, bottom-up, and top-down search heuristics) and allows the modular addition of specialized heuristics for the speech understanding task.

The Hearsay II speech understanding system being developed at Carnegie-Mellon University has an independent knowledge source module for each type of speech knowledge. Modules communicate by reading, writing, and modifying hypotheses about various constituents of the spoken utterance in a global data structure. The syntax and semantics module uses rules (productions) of four types: (1) recognition rules for generating a phrase hypothesis when its needed constituents have already been hypothesized; (2) prediction rules for inferring the likely presence of a word or phrase from previously recognized portions of the utterance; (3) respelling rules for hypothesizing the constituents of a predicted phrase; and (4) postdiction rules for supporting an existing hypothesis on the basis of additional confirming evidence. The rules are automatically generated from a declarative (Le., non-procedural) description of the grammar and semantics, and are embedded in a parallel recognition network for efficient retrieval of applicable rules. The current grammar uses a 450-word vocabulary and accepts simple English queries for an information retrieval system.