Kurt Hungerford CSCI 8110

The Knowledge Machine is a knowledge representation and reasoning system that allows users to store concepts and relationships and then perform inferences on the knowledge base.

KR&R – Knowledge Representation and Reasoning Description of the KM KM Applications

KM website: Knowledge Systems Research Group website: KM Manual [Barker K., et. al.] A Question-Answering System for AP Chemistry: Assessing KR&R Technologies The KM Algorithm (powerpoint) KM Tutorial (powerpoint)

Knowledge Representation Reasoning More organized way for computers to represent how people think Computers are ignorant… Our goal is to build knowledgeable computers – capable of conversing intelligently on many topics. - Knowledge Systems Research Group

Symbols Represent objects and concepts Example: chess Represent the board Represent the pieces Represent positioning Knowledge Base – statements about what we know and believe

Inference – using current knowledge to deduce new knowledge Example: chess What will the board look like if I make a particular move? What will be the best response from my opponent? Given that, what is my best response?

Developed by the Knowledge Systems Research Group at University of Texas Austin Knowledge Representation Language Implemented in LISP Represents Knowledge in Frames Inference-Capable

Object-Oriented Frames and Slots Similar to Classes and Fields Queries Retrieve stored knowledge Perform inferences on knowledge

Object Information about the object Syntax: (every has (slot1 (expr1 expr2 …)) (slot2 (expr1 expr2 …)) …)

Frames have slots Slots are how relations between concepts are represented Predicates about the frame Slots assert what is known about the frame

(every Building has (doors (front back)) (windows (w1 w2 w3 w4)) (roof (r1))) (myHouse has (instance-of (Building))) (myHouse has (doors (side1 side2))) (myHouse2 has (instance-of (Building)))

Instances that get automatically, created by the KM (a ) returns an anonymous instance Example: (a Building) (_Building15)

Knowledge Look-Up Inference Syntax (the of )

(the doors of *myHouse) (side1 side2 front back) (the doors of *myHouse2) (front back)

An atomic value returns itself (4) -> 4 Otherwise, decompose the expression Decomposition results in smaller expressions, which are then recursively evaluated Ultimately, this will return a value, which is then propagated back up the recursion chain

Different kinds of expressions decompose slightly differently Example: (if then ) => (expr1) returns bool1 if bool1 = true then (expr2) (the of ) => (expr) returns frame; (the of frame)

Working with lists (it is LISP, after all) KM only computes slots on demand Unification == for unification; /== for doesnt unify = for testing equality; /= for testing inequality (t) used for true; NIL used for false Any non-NIL value also evaluates to true Output precision works in scientific notation Delete – doesnt undo previous inferences

Constraints Prototypes Theories Situations Simulations Metaclasses

Botany Knowledge Base Large Botany KB Used early version of KM Project Halo Expert Tutor Store knowledge base on different subjects Answered users questions and provide explanation

Effort to create a Digital Aristotle Expert Tutor Wide variety of subjects Example: Chemistry Attempt to develop a system capable of taking the AP Chemistry exam

Develop an expert system for AP Chemistry Focused on a subset of Chemistry: Stoichiometry and equilibrium reactions Needed to restrict the domain, while still working with a wide variety of questions System needed to deal with a wide variety of questions Also needed to be able to provide explanations for the answers it gave

Questions posed in using KM Answers used two types of reasoning: Automatic classification – introduce new concepts by using definitions (based off chemistry terms) Backward chaining – goal-oriented search method (based off chemistry laws)

The system also provided explanations with its answers KM logs the rules it uses during its reasoning The Chemistry application uses that record to generate a human-readable explanation The explanation leaves out the uninteresting parts of the reasoning process This results in a succinct, understandable derivation of the answer

KM is a KR&R Language Used to capture knowledge about a domain Used to reason about knowledge Provide an explanation of its reasoning