Artificial Intelligence 6:361-371

IJCAI S, 23-29.

A problem domain can be represented as a hierarchy of abstraction spaces in which successively finer levels of detail are introduced. The problem solver ABSTRIPS, a modification of STRIPS, can define an abstraction space hierarchy from the STRIPS representation of a problem domain, and it can utilize the hierarchy in solving problems. Examples of the system's performance are presented that demonstrate the significant increases in problem-solving power that this approach provides. Then some further Implications of the hierarchical planning approach are explored.Later journal article in Artificial Intelligence 5:115-135 (1974). Available for a fee. In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

The design of application of artificia l intelli­gence to a scientific task such as Organic Chemical Synthesis was the topic of a Doctoral Thesis completed in the summer of 197I. Chemical synthesis in practice involves i) the choice of molecule to be synthesized; i i) the formulation and specification of a plan for synthesis (involving a valid reaction pathway leading from commercial or readily available compounds to the target compounds with consideration of feasibility regarding the purposes of synthesis);iii ) the selection of specific individual steps of reaction and their temporal ordering for execution; iv) the exper­imental execution of the synthesis and v) the redesign of syntheses, if necessary, depending upon the exper­imental results. In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

In IJCAI-73, pp. 1–11.

Dept. of Computer Science, Carnegie-Mellon University. "Many game-playing programs must search very large game trees. Use of the alpha-beta pruning algorithm instead of the simple minimax search reduces by a large factor the number of bottom positions which must be examined in the search. An analytical expression for the expected number of bottom positions examined in a game tree using alpha-beta pruning is derived, subject to the assumptions that the branching factor N and the depth D of the tree are arbitrary but fixed, and the bottom positions are a random permutation of ND unique values. A simple approximation to the growth rate of the expected number of bottom positions examined is suggested, based on a Monte Carlo simulation for large values of N and D. The behavior of the model is compared with the behavior of the alpha-beta algorithm in a chess playing program and the effects of correlation and non-unique bottom position values in real game trees are examined."

Machine Intelligence 7

See also: Robert Kowalski. 1975. A Proof Procedure Using Connection Graphs. J. ACM 22, 4 (October 1975), 572-595.In B. Meltzer and D. Michie (Eds.), Machine intelligence 7. New York: Wiley, 167-194

Reprinted in Readings in Planning, edited by J. Allen, J. Hendler, and A. Tate, Morgan Kaufmann Publishers, San Mateo, California, 1990. Also Reprinted in Computation and Intelligence: Collected Readings, edited by George F. Luger, AAAI Press, 1995. See also: Artificial Intelligence, Volume 2, Issues 3–4, Winter 1971, Pages 189–208 In IJCAI-71: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE. British Computer Society, London.. Revised version in Artificial Intelligence, 2(3), pp 189-208.

One of the central principles upon which intelligent devices seem to operate is that of maintaining internal models of their external environments. How difficult this is, depends upon both the complexity of the model and its method of representation. In particular, it is usually easy when the problem is posed in the classical heuristic search paradigm, and the data structures used to represent static configurations of the puzzle are relatively unproblematic (arrays, lists, and so on). The lack of side-effects reflects the simplicity of the physics which such models embody. This limitation to elementary forms of interaction is not, of course, intrinsic to the heuristic search method; but when more complex models are constructed it becomes less trivial to pursue the consequences of performing an action. This approach is more general than the heuristic search method (but the latter -- when it has sufficient expressive power -- wins at present by its computational advantage). Assertions mentioning several different situations can then be used to describe dynamical laws which move us from one situation to another. But in some ways the resulting sharp separations between states of affairs are an embarrassment. For if we distinguish two situations s1 and s2, then from the fact, if such it be, that a predicate p is true of Si, nothing whatever follows concerning s2. And this is true even when s2 is directly associated with sl. Say s2 results from s1 by the performance of some action: s2 do (a, si) then no matter how remote -- speaking intuitively -- the connection between the property p and the action a, it still does not follow that p is true of s2. If we want it to so follow we must state this explicitly. Now, unfortunately, there are innumerable facts which might remain unchanged when actions are performed. So instead of writing a law of motion' in the form A(s) B(do(a, s)) where A and B are fairly short expressions, we are apparently obliged to list systematically all conceivable facts which are not changed. So that the law looks more like (Ci(s)& Ci(do(a, s))& & C„(do(a, s))&B(do(a, s)) for some very large n. This works for small problems (such as the familiar hungry anthropoid), but these are usually better formalized in the heuristic search paradigm anyway.