https://onlinelibrary.wiley.com/doi/pdf/10.1207/s15516709cog0101_2

An overview of KRL, a Knowledge Representation Language

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 strategies) and allows the modular addition of specialized heuristics for the speech understanding task. Empirical results demonstrate the effectiveness of the focusing principles, and possible directions for future research are considered.

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Focus of attention in the Hearsay-II speech understanding system

The Hearsay‐II System has as its design goal recognition, understanding, and responding to connected speech utterances, particularly in situations where sentences cannot be guaranteed to agree with some predefined, restricted language model, as in the case of the Harpy System. Further, it attempts to view knowledge sources as different and independent which cannot always be integrated into single representation. It is based on the blackboard model [V. R. Lesser, R. D. Fennell, L. D. Erman, and D. R. Reddy, IEEE Trans. Acoust. Speech and Signal Process. ASSP‐23, 11–23 (1975) with knowledge sources as a set of parallel processes which are activated asynchronously depending on data events. The system performs on the Information Retrieval task with accuracy comparable to that of the Harpy system, but runs about 2 to 20 times slower. More complete performance results will be reported. As we get closer to unrestricted vocabularies and nongrammaticality of spoken languages, it will be necessary to have systems w...

The Hearsay‐II speech understanding system

Computer Models of Speech Using Fuzzy Algorithms

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.

Focus of attention in a distributed-logic speech understanding system

: Two important problems in speech understanding are how to effectively integrate multiple sources of knowledge within the system and how to control the activities of the system to arrive at appropriate interpretations tor utterances. This paper first describes the roles played by acoustics, syntax, semantics, and discourse, and shows how a language definition is used to integrate them into a system in a way that allows the interactions to be easily visible. The second part of the paper describes an executive that uses information from these knowledge sources in its control strategy,

System Integration and Control in a Speech Understanding System

T h i s paper d e s c r i b e s t h e p r o c e d u r e s f o r i n t e g r a t i n g knowledge f rom d i f f e r e n t s o u r c e s i n t h e SRI speech u n d e r s t a n d i n g s y s t e m . A language d e f i n i t i o n sys tem c o o r d i n a t e s — a t t h e ph rase l e v e l — i n f o r m a t i o n f rom s y n t a x , s e m a n t i c s and d i s c o u r s e i n t h e cou rse o f t h e i n t e r p r e t a t i o n o f a n u t t e r a n c e . The sys tem e x e c u t i v e uses t h e s e c o n t e x t u a l c o n s t r a i n t s i n a s s i g n i n g p r i o r i t i e s t o a l t e r n a t i v e i n t e r p r e t a t i o n s , c o m b i n i n g t o p d o w n , b o t t o m u p , and b i d i r e c t i o n a l s t r a t e g i e s a s r e q u i r e d E x p e r i m e n t a l r e s u l t s t h a t d e m o n s t r a t e t h e e f f e c t i v e n e s s o f c o n t e x t c h e c k i n g a r e d i s c u s s e d .

/pdf/procedures-for-integrating-knowledge-in-a-speech-plngytse9a.pdf

Procedures for integrating knowledge in a speech understanding system

This note outlines research on natural language communication between two parties « cooperating to accomplish a task. Our goal is to formalize the knowledge needed to understand task-oriented dialogs and to develop techniques for representing and coordinating this knowledge in a computer system that can participate in the dialog. Encoding general process knowledge and the dynamics of specific tasks in a manner that is useful for the interpretation and generation of utterances in a dialog is a central issue in this research. A task orientation provides a framework for studying the influence of context on language. Our previous analyses of several task-oriented dialogs (e.g., Deutsch, 1974 indicate (1) The context of an utterance includes at least three discernable factors: the preceding utterances in the dialog, the state of the task, and the physical environment of the dialog. (2) Task-oriented dialogs have a structure that reflects the structure of the task. The set of objects and actions that are most relevant to the conversation, and hence in the focus of attention of the dialog participants, changes as the task progresses. To follow such shifts in focus, a computational system needs a model of the task being performed. To meet this need, we have developed a representation scheme for encoding process knowledge and are using it to encode data about specific tasks. This scheme is used both to answer particular questions about the task and to guide linguistic processing. The representation combines features of the partitioned semantic networks of Hendrix (1975), the procedural networks of interest is the fact that those objects that are in focus at a given time are clearly delineated. We are currently developing algorithms that use this representation of task knowledge to expand the use of context in the interpretation of inputs and the generation of responses, instructions, and requests. These algorithms include mechanisms that follow the shift of focus with the progress of the task.

/pdf/using-process-knowledge-in-understanding-task-oriented-2s1bzpxr83.pdf

Using process knowledge in understanding task-oriented dialogs

One hundred applications of artificial-intelligence technology and robotics in Army combat and combat support that may be possible and worthwhile are identified. These possible applications have been divided into ten categories, and one example in each category has been examined in detail. Research and development plans have been developed showing the basic and applied research that would be needed to make the applications possible. Although the number of possible applications is large, the number of key technology elements is relatively small, and many of the same technology elements are required in many different applications.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Ann E. Robinson

Papers

System Integration and Control in a Speech Understanding System

Procedures for integrating knowledge in a speech understanding system

Using process knowledge in understanding task-oriented dialogs

Applications Of Artificial Intelligence/Robotics