CSCE 580 Artificial Intelligence Ch.5 [P]: Propositions and Inference Sections 5.5-5.7: Complete Knowledge Assumption, Abduction, and Causal Models. Fall 2009 Marco Valtorta firstname.lastname@example.org. Acknowledgment. The slides are based on [AIMA] and other sources, including other fine textbooks
CSCE 580Artificial IntelligenceCh.5 [P]: Propositions and InferenceSections 5.5-5.7: Complete Knowledge Assumption, Abduction, and Causal Models
Abduction is a form of reasoning where assumptions are made to explain observations.
A causal network
Determining what is going on inside a system based on observations about the behavior is the problem of diagnosis or recognition.
In abductive diagnosis, we need to axiomatize what follows from faults as well as from normality assumptions. For each atom that could be observed, we axiomatize how it could be produced.
This could be seen in design terms as a way to make sure the light is on: put both switches up or both switches down, and ensure the switches all work. It could also be seen as a way to determine what is going on if the agent observed l1 is lit: one of these two scenarios must hold.
The bottom-up and top-down implementations for assumption-based reasoning with Horn clauses (page 190) can both be used for abduction.
There are many decisions the designer of an agent needs to make when designing knowledge base for a domain. For example, consider two propositions a and b, both of which are true. There are many choices of how to write this.
In order to predict the effect of interventions, a causal model represents how the cause implies its effect. When the cause is changed, its effect should be changed. An evidential model represents a domain in the other direction,from effect to cause.