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 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.>

Current high-accuracy speech understanding systems achieve their performance at the cost of highly constrained grammars over relatively small vocabularies. Less-constrained systems will need to compensate for their loss of top-down constraint by improving bottom-up performance. To do this, they will need to eliminate from consideration at each place in the utterance most words in their vocabularies solely on the basis of acoustic information and expected pronunciations of the words. Towards this goal, we present the design and performance of Noah, a bottom-up word hypothesizer which is capable of handling large vocabularies-more than 10 000 words. Noah takes (machine) segmented and labeled speech as input and produces word hypotheses. The primary concern of this work is the problem of word hypothesizing from large vocabularies. Particular attention has been paid to accuracy, knowledge representation, knowledge acquisition, and flexibility. In this paper we discuss the problem of word hypothesizing, describe how the design of Noah faces these problems, and present the performance of Noah as a function of the vocabulary size.

The range of application areas to which distributed processing has been applied effectively is limited. In order to extend this range, new models for organizing distributed systems must be developed.

This paper considers various factors affecting system em organization for speech understanding research. The structure of the Hearsay system based on a set of cooperating, independent processes using the hypothesize-and-test paradigm is presented. Design considerations for the effective use of multiprocessor and network achitectures in speech understanding systems ems are presented: control of processes, interprocess communication and data sharing, resource allocation, and debugging are discussed.1

This paper describes the structure and operation of the Hearsay-I1speech understanding system by the use of a specific example illustrating the various stages of recognition. The system consists of a set of cooperating independent processes, each representing a source of knowledge. The knowledge is used either to predict what may appear in a given context or to verify hypotheses resulting from a prediction. The structure of the system is illustrated by considering its operation in a particular task situation: Voice-Chess. The representation and use of various sources of knowledge are outlined. Preliminary results of the reduction in search resulting from the use of various sources of knowledge are given.