The hot list strategy.
Experimentation strongly suggests that, for attacking deep questions and hard problems with the assistance of an automated reasoning program, the more effective paradigms rely on the retention of deduced information. A significant obstacle ordinarily presented by such a paradigm is the deduction and retention of one or more needed conclusions whose complexity sharply delays their consideration. To mitigate the severity of the cited obstacle, I formulated and feature in this article the hot list strategy. The hot list strategy asks the researcher to choose, usually from among the input statements characterizing the problem under study, one or more statements that are conjectured to play a key role for assignment completion. The chosen statements--conjectured to merit revisiting, again and again--are placed in an input list of statements, called the hot list. When an automated reasoning program has decided to retain a new conclusion C--before any other statement is chosen to initiate conclusion drawing--the presence of a nonempty hot list (with an appropriate assignment of the input parameter known as heat) causes each inference rule in use to be applied to C together with the appropriate number of members of the hot list. Members of the hot list are used to complete applications of inference rules and not to initiate applications. The use of the hot list strategy thus enables an automated reasoning program to briefly consider a newly retained conclusion whose complexity would otherwise prevent its use for perhaps many CPU-hours. To give evidence of the value of the strategy, I focus on four contexts: (1) dramatically reducing the CPU time required to reach a desired goal, (2) finding a proof of a theorem that had previously resisted all but the more inventive automated attempts, (3) discovering a proof that is more elegant than previously known, and (4) answering a question that had steadfastly eluded researchers relying on an automated reasoning program. I also discuss a related strategy, the dynamic hot list strategy (formulated by my colleague W. McCune), that enables the program during a run to augment the contents of the hot list. In the Appendix, I give useful input files and interesting proofs. Because of frequent requests to do so, I include challenge problems to consider, commentary on my approach to experimentation and research, and suggestions to guide one in the use of McCunes automated reasoning program OTTER.
- Research Organization:
- Argonne National Lab. (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- ER
- DOE Contract Number:
- DE-AC02-06CH11357
- OSTI ID:
- 953395
- Report Number(s):
- ANL/MCS/JA-28206; JAREEW; TRN: US200915%%60
- Journal Information:
- J. Automat. Reason., Vol. 22, Issue 1999; ISSN 0168-7433
- Country of Publication:
- United States
- Language:
- ENGLISH
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