In Walker, D. E. & Norton, L. N. (eds. ), IJCAI 1969: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, May 7-9, 1969 Washington, D. C., pp. 219-39.

In IJCAI 1969: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, May 7-9, 1969 Washington, D. C.

PLANNER is a language for proving theorems and manipulating models in a robot. The language is built out of a number of problem solving primitives together with a hierarchical control structure. Statements can be asserted and perhaps later withdrawn as the state of the world changes. Conclusions can be drawn from these various changes in state. Goals can be established and dismissed when they are satisfied . The deductive system of PLANNER is subordinate to the hierarchical control structure in order to make the language efficient . The use of a general purpose matching language makes the deductive system more powerful. In IJCAI-69, pp. 295-€“301.

This paper shows how a question -answering system can be constructed using first -order logic as its language and a resolution -type theorem -prover as its deductive mechanism. A working computer -program, Q A3, based on these ideas is described. The performance of the program compares favorably with several other general question -answering systems.

In Meltzer, Bernard and Donald Michie (Eds.). Machine Intelligence 5, pp. 78-98. Edinburgh University Press.

"A computer program has been written which can formulate hypotheses from a given set of scientific data. The data consist of the mass spectrum and the empirical formula of an organic chemical compound. The hypotheses which are produced describe molecular structures which are plausible explanations of the data. The hypotheses are generated systematically within the program's theory of chemical stability and within limiting constraints which are inferred from the data by heuristic rules. The program excludes hypotheses inconsistent with the data and lists its candidate explanatory hypotheses in order of decreasing plausibility. The computer program is heuristic in that it searches for plausible hypotheses in a small subset of the total hypothesis space according to heuristic rules learned from chemists."In Meltzer, B., Michie, D., and Swann, M. (Eds.), Machine Intelligence 4, pp. 209-254. Edinburgh University Press

See also: Education Resources Information CenterSupplement to Proceedings of the IFIP 68 International Congress, Edinburgh, August 1968. Published in A. J. H. Morrell (ed.), Information Processing 68, Vol. II, pp. 1008-1022, Amsterdam: North-Holland, 1969.

IJCAI 1, 557-567.

Stanford AI Memo 83 or Computer Science Technical Note 130, Computer Science Department, Stanford University, Stanford, CA, 1969

Reading, Mass.: Addison-Wesley.