Genesereth, M. R. | Nourbakhsh, I.
The idea is worked out for the cases of sequential planning, conditional planning, and interleaved planning and execution. In much of the early literature on robot problem solving, the problem solver is assumed to have complete information about the initial state of the world. In our version, the robot's job is to achieve a goal G, when started in an environment The traditional approach to problem solving with complete information is sequential planning and execution. To find a plan, the robot searches the environment's state graph for a path connecting its single initial state to a goal state.
"Loosely speaking, recursive inference occurs when an inference procedure generates an infinite sequence of similar subgoals. In general, the control of recursive inference involves demonstrating that recursive portions of a search space will not contribute any new answers to the problem beyond a certain level. We first review a well-known syntactic method for controlling repeating inference (inference where the conjuncts processed are instances of their ancestors), provide a proof that it is correct, and discuss the conditions under which the strategy is optimal. We also derive more powerful pruning theorems for cases involving transitivity axioms and cases involving subsumed subgoals. The treatment of repeating inference is followed by consideration of the more difficult problem of recursive inference that does not repeat. Here we show how knowledge of the properties of the relations involved and knowledge about the contents of the system's database can be used to prove that portions of a search space will not contribute any new answers." Artificial Intelligence, 30 (3), 343-89.
Genesereth, M. R.
This paper describes a device-independent diagnostic program called dart. The resulting generality allows it to be applied to a wide class of devices ranging from digital logic to nuclear reactors. Although this generality engenders some computational overhead on small problems, it facilitates the use of multiple design descriptions and thereby makes possible combinatoric savings that more than offsets this overhead on problems of realistic size.
Genesereth, M. R. | Smith, D. E.
An Overview of Meta-Level Architecture Michael R. Genesereth Stanford University Computer Science Department Stanford, California 94305 Abstract: One of the biggest problems in AT programming is the difficulty of specifying control. In a data base management system, data base accesses and modifications would be base-level actions, and:;ll query optimiTation would be mcta-level. In an autonralcd deduction system, inf2rencc steps would be base-level, and the activity of deciding the order in which to perform infcrencc steps would be meta-level. 1 tie central idea in meta-level architecture is the use of a declarative language for describing behavior.