In L. Steels (Ed.), Advances in natural language processing. Antwerp, Belgium: University of Antwerp.

In L. Steels (Ed.), Advances in natural language processing. Antwerp, Belgium: University of Antwerp.

This paper presents a functional overview of the features and capabilities of QLISP, one of the newest of the current generation of very high level languages developed for use in Artificial Intelligence (AI) research.QLISP is both a programming language and an interactive programming environment. It embeds an extended version of QA4, an earlier AI language, in INTERLISP, a widely available version of LISP with a variety of sophisticated programming aids.The language features provided by QLISP include a variety of useful data types, an associative data base for the storage and retrieval of expressions, the ability to associate property lists with arbitrary expressions, a powerful pattern matcher based on a unification algorithm, pattern-directed function invocation, "teams" of pattern invoked functions, a sophisticated mechanism for breaking a data base into contexts, generators for associative data retrieval, and easy extensibility.System features available in QLISP include a very smooth interaction with the underlying INTERLISP language, a facility for aggregating multiple pattern matches, and features for interactive control of programs.A number of applications to which QLISP has been put are briefly discussed, and some directions for future development are presented. SRI Tech.Note 120, AI Center, SRI International, Inc., Menlo Park, Calif.

SRI Tech. Note 131, AI Center, SRI International, Inc., Menlo Park, Calif.

Zeitschrift fur Operations Research, Serie A, 20 (5), 203-208.

See also: IBM J. Research and Development 20:326-335.

In Davis and Lenat (1980). Memo AIM-283, AILaboratory,and Rep.No.STAN-CS-76-552, Computer Science Dept., Stanford University.

The Harpy connected speech recognition system is the result of an attempt to understand the relative importance of various design choices of two earlier speech recognition systems developed at Carnegie-Mellon University: The Hearsay-1 system and the Dragon system. Knowledge is represented in the Hearsay-1 system as procedures and in the Dragon system as a Markov network with a-priori transition probabilities between states. Systematic performance analysis of various design choices of these two systems resulted in the HARPY system, in which knowledge is represented as a finite state transition network but without the a-priori transition probabilities. Harpy searches only a few'best' syntactic (and acoustic) paths in parallel to determine the optimal path, and uses segmentation to effectively reduce the utterance length, thereby reducing the number of state probability updates that must be done. Several new heuristics have been added to the HARPY system to improve its performance and speed: detection of common sub-nets and collapsing them to reduce overall network size and complexity, eliminating the need for doing an acoustic match for all phonemic types at every time sample, and semi-automatic techniques for learning the lexical representations (that are needed for a steady-state system of this type) and the phonemic templates from training data, thus automatically accounting for the commonly occurring intra-word coarticulation and juncture phenomena.

Boitet, C. 1977 Where Does GETA Stand at the Beginning of Brinkmann, K.-H.

A generalization of AND/OR graphs is introduced as a problem solving model, in which subproblem interdependence in problem reduction can be explicitly accounted for. An ordered-search algorithm is given to find a solution. The algorithm is proven to be admissible and optimal. Examples are given which show the application of the formalism to problems which cannot be modelled by AND/OR graphs. Generalized AND/OR graphs are finally shown to be equivalent to type O grammars.