Abstract
Efficiency and robustness are two desirable, but often conflicting, goals of problem solvers. This paper examines how a combination of associational and causal reasoning can be used to achieve both goals. We describe the Generate, Test and Debug (GTD) paradigm, which uses associational reasoning to solve most problems efficiently, while relying on causal reasoning to maintain overall robustness. The problem-solving characteristics of associational and causal reasoning are presented, based on an analysis of the types of knowledge and reasoning used in GTD. In particular, we argue that the characteristics depend largely on the extent to which interactions between events are represented and reasoned about — associational reasoning is efficient because it uses rules that (nearly) encapsulate interactions, while causal reasoning is robust because it analyzes the effects of events and their interactions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chandrasekaran, B.: Towards a functional architecture for intelligence based on generic information processing tasks. Proc. IJCAI-87, Milan, Italy (1987)
Chapman, D.: Planning for conjunctive goals. Artificial Intelligence 32 (1987) 333–377
Hammond, K.: Case-Based Planning: Viewing Planning as a Memory Task. Academic Press, (1989)
Koton, P.: Combining causal models and case-based reasoning. in Second Generation Expert Systems, David, Krivine and Simmons (eds.), Springer Verlag (1993)
Rich, C. and Waters, R.: Abstraction, inspection and debugging in programming. MIT AI Memo 634 (1981)
Sacerdoti, E.: A Structure for Plans and Behavior. American Elsevier (1977)
Simmons, R.: Representing and reasoning about change in geologic interpretation. MIT AI Technical Report 749 (1983)
Simmons, R.: A theory of debugging plans and interpretations. Proc. AAAI-88, St. Paul, MN (1988)
Simmons, R.: Combining associational and causal reasoning to solve interpretation and planning problems. MIT AI Technical Report 1048 (PhD Thesis) (1988)
Simmons, R.: Integrating Multiple Representations for Incremental, Causal Simulation. Proc. Conference on AI, Simulation, and Planning, Cocoa Beach, FL (1991) 88–96
Simmons, R.: The roles of associational and causal reasoning in problem solving. Artificial Intelligence 53:2–3 (1992) 159–208
Simmons, R. and Davis, R.: Generate, test and debug: Combining associational rules and causal models. Proc. IJCAI-87, Milan, Italy (1987)
Simmons, R. and Mohammed, J.: Causal modeling of semiconductor fabrication. International Journal for Artificial Intelligence in Engineering 4:1 (1989) 2–21
Simon, H.: The Sciences of the Artificial. MIT Press (1969)
Sussman, G.: A computer model of skill acquisition. American Elsevier (1977)
Torasso, P. and Console, L.: Diagnostic Problem Solving: Combining Heuristic, Approximate and Causal Reasoning. Van Nostrand Reinhold, (1989)
Williams, B.: A theory of interactions: Unifying qualitative and quantitative algebraic reasoning. Artificial Intelligence 51:1–3 (1991) 39–94
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Simmons, R. (1993). Generate, Test and Debug: A Paradigm for Combining Associational and Causal Reasoning. In: David, JM., Krivine, JP., Simmons, R. (eds) Second Generation Expert Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77927-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-77927-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-77929-9
Online ISBN: 978-3-642-77927-5
eBook Packages: Springer Book Archive