1 / 11

Intro to Computation and AI

This lecture provides an overview of rule-based systems in Artificial Intelligence (AI), including their knowledge structure and processing mechanisms. It explores the use of rules to solve problems or tasks in a domain and discusses the behavior and expressiveness of rule languages. The lecture also highlights some issues in using rule-based systems, such as knowledge acquisition and explanation.

albertomason
Download Presentation

Intro to Computation and AI

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Intro to Computation and AI Dr. Jill Fain Lehman School of Computer Science Lecture 6: December 4, 1997

  2. Review • Last lecture looked at AI uses of basic graph structures and algorithms: • Inheritance hierarchies and inferential reasoning • Constraint satisfaction and propagation • In addition to relying on basic CS structures and algorithms, AI has also created its own formalisms: e.g. rule-based systems.

  3. Rule-based Systems • Formalism critical in 1980’s for moving AI out of the lab and into practical industry applications. • Basis of Expert Systems & Expert System Shells. • Also basis of 2 long-standing cognitive architectures: ACT and Soar. • 2 parts: knowledge structure and processing mechanism (of course).

  4. Knowledge Structure • Rules encode the knowledge of the domain required to solve problems or tasks in the domain. • Conceptually, a rule is made up of 2 parts: • The “if” portion (aka left-hand side, conditions or preconditions) • The “then” portion (aka right-hand side, actions, effects). • There are many variations on this theme, e.g. including a “confidence measure” with a rule to help accommodate uncertainty.

  5. Example: MYCIN (Shortliffe) An expert system for guiding bacterial infection therapy. Premise: ($and (same cntxt gram grampos) (same contxt morph coccus) (same cntxt conform clumps)) Action: (conclude cntxt ident staphylococcus tally 0.7) If (1) the stain of the organism is gram-positive, and (2) the morphology of the organism is coccus, and (3) the growth conformation of the organism is clumps,l Then there is suggestive evidence (0.7) that the identity of the organism is staphylococcus.

  6. Example: R1 (McDermott) If the most current active context is distributing massbus devices, & there is a single-port drive that hasn’t been assigned to a massbus, & there are no unassigned dual-port disk drives, & the number of devices that each massbus should support is known, & there is a massbus that has been assigned at least 1 drive that should support additional disk drives, & the type of cable needed to connect the disk drive to the previous device on the massbus is known Then assign the disk drive to the massbus.

  7. Processing Mechanism • Conceptually: a rule interpreter that matches the “if” portion of the rule against a state description and then changes the state description based on the “then” portion. • Exactly what goes in the state and what goes in the rule and what goes in the interpreter is the basis of the differences between particular rule systems.

  8. Simple Example • Suppose state had 2 objects: blocka and blockb and blocka was on the table and blockb was on top of blocka. State: {(isa block blocka) (isa block blockb) (ontop blockb blocka) (ontable blocka)} Goal: {(ontable blockb)} Operator-move-off: If (state (isa block <x>) (ontop <x> <y>))(goal (ontable <x>)) Then (add (ontable <x>)) (remove (ontop <x> <y>)) What does the interpreter do???

  9. Expressiveness of Rule Language • Need to have variables that can be bound to particular instances during problem solving. • Usually need logical operations (not & disjunction) in addition to implicit conjunction). • Need to encode directions to the interpreter (e.g. “add” “remove”). • May need metalanguage (ability to talk about rules themselves) (this may also be hidden in the interpreter).

  10. Behavior of Interpreter • Enforce consistency of bindings across clauses. • Must match conditions efficiently: • Large numbers of rules cannot be searched linearly: need some form of indexing • trade-off betw. expressiveness of language/ease of match • Needs conflict resolution strategy when more than one rule can fire. • E.g. fire all vs. fire exactly one (based on: rules, objects, or actions?) • May require additional capabilities to, e.g., combine certainty factors.

  11. Some Issues in Using Rule-based Systems • Knowledge acquisition: how to get the expertise out of experts, how to represent it efficiently, how to maintain consistency, how to check for completeness. • Iterative approach, lots of time spent on tools. • Explanation: how to know what the system is doing, how to assure experts that reasons for behavior are appropriate. • Alternatives to NL? • Ethics

More Related