Knowledge-Based SystemsINFO612

Professor: Dr. Rosina Weber

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What is AI (from R&N)

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What are knowledge-based systems?

Systems that manipulate knowledge and reasoning to solve problems rationally.

Examples of knowledge-based systems

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Introduction to knowledge-based systems -KBS-

GPS input: problem output: solution expertise how to represent

expertise?

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How to represent expertise?

•represent knowledge

•represent reasoning

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Represent knowledge? What is knowledge?

Knowledge representation formalisms• rules• cases• semantic nets• frames

procedural knowledge

declarative knowledge

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Represent reasoning

deductive reasoninginductive reasoninganalogical reasoningabductive reasoning

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How to represent and reason with knowledge in a computer

program? (i)

• Algorithms and methodologies that control the proper application of knowledge towards its intended

result.• For example, case-based reasoning, expert systems,

ontologies

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How to represent and reason with knowledge in a computer

program? (ii)

• These methodologies reason with knowledge differently.

• When reasoning with knowledge to solve a problem, these

methodologies perform tasks.

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AI tasks

• reading &understanding

• diagnosis• configuration• categorizatio

n• classification• creativity• discovery

• speech recognition & synthesis

• obstacle avoidance

• NL generation

• NL understanding

• planning• scheduling• design• prediction• control• monitoring• analysis• vision

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Knowledge-based methodologies

Case-based reasoningExpert systemsOntologies

Three different methods to organize knowledge and reason with it to perform a multitude of AI tasks

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Expert SystemsES are a methodology to develop computer programs that manipulate expertise to solve expert problems in specific domains. Rule-based expert systems represent knowledge through rules.

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Expert SystemsA computer program designed to model the problem-solving ability of a human expert. (John Durkin, 1994)

Working memory

Knowledge base

Inference engine

Problem description

solution

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expertsolutionexpert

solution

Expert Systems Methodology

knowledgebase

(e.g.,framesand methods)

knowledgebase

(e.g.,framesand methods)

explanationexplanation

generalknowledgegeneral

knowledge

userInterface

userInterface

expertproblemexpert

probleminferenceengine

(agenda)

inferenceengine

(agenda)

working memory(short-term mem/information)

working memory(short-term mem/information)

Knowledge acquisitionKnowledge acquisition

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Expert Systems: history

began 1965 at StanfordDENDRAL: a system that uses heuristics to generate structures of data to perform chemical analysis of the Martian soil and works as well as an expert chemist;the first program recognized to have succeeded due to the knowledge it contained instead of complex search techniques;

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Expert Systems: types

Rule-Based Expert Systemsbackward-chaining or forward-chaining

Logic-based (including using Fuzzy Logic)Frame-Based Expert SystemsHybrid Expert SystemsObject-Oriented Expert Systems

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Expert Systems: applications

Areas: agriculture, business, chemistry, communications, computer systems, education, electronics, engineering, law, manufacturing, mathematics, medicine, transportation, etc.Tasks: analysis, control, design, diagnosis, instruction, interpretation, monitoring, planning, prediction, prescription, selection and simulation.

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The similarity heuristicthe reminding of a past episode that is similar to a current one so that one can apply a strategy/solution that has worked in a similar episode

CBR assumptions

•similar problems have similar solutions•problems recur (Leake, 1996)

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The CBR cycleRetrieveReuseRevise(Review)Retain

name task author obs.

ABBY Romantic advisor; retrieves a similar history

Domeshek

Social context

ALFA Predict power demand Jabour Same result but faster than human experts

ARCHIE Architecture design of office buildings

Goel, Kolodner

 

CADET Design of mechanical components

Sycara, Navinchandra

Abstract indexing allowed innovative design

CASEY Diagnosis cause and prescribes solution to heart problems

Koton model-based

Compaq SMART

Diagnosis and repair; customer support help desks

Acorn, Walden

Uses Inference’s tool; can be used by up to 60 users at a time; shows that library engineering is necessary

CHEF Design of recipes to meet different simultaneous goals

Hammond Memory started with 20 recipes and learned from user feedback

CLAVIER Design and evaluation of autoclave loading

Barletta & Hennessy

Interacts planning and scheduling

COACH Planning soccer games Collins Debugging and fixing bad strategies; memory keeps strategies and the type of problem

HYPO Interpretation and argumentation

Rissland & Ashley

Retrieves similar cases to create a point, a response, and a rebuttal using hypotheticals (Ashley, 1990)

JUDGE Defines sentences of delinquent crimes based on the chances of repeating the crime and its severity 

Bain In case of not having a sufficient similar case, the system uses heuristics to determine the sentence

name task author obs.

MEDIATOR

Mediates conflicts by performing planning

Simpson Keeps in memory failed solutions and tries to avoid same failures in new solutions

PERSUADER

Mediation of union negotiations; proposes solutions with arguments

Sycara Considers part’s goals and considers recent accepted solutions

JULIA Desing of meal planning Hinrichs Plausible reasoning and design

PLEXUS Planning daily tasks Alterman Adapts the experience of riding the SF metro to reuse in NY

PRODIGY Planning and learning Veloso, Carbonell

Demonstrated in a variety of domains

PROTOS Heuristic classification for diagnosis

Bareiss, Porter, Murray, Weir, Holte

Automatic knowledge acquisition; good for weak theory domains

SQUAD Software quality control advisor

Kitano 20,000 cases in 1993

SWALE Generates explanation of anomalous events in news stories

Schank, Kass, Leake, Owens

Searches for similar explanations for death and destruction such as the murdered spouse that was killed because of the insurance money just like the horse (SWALE) that was killed by its owner for the same reason  Mostly from Kolodner 1993    

name task author obs.

CATO Tutoring system Aleven/Ashley

Teaching law students to create argument

PRUDENTIA Jurisprudence research; textual CBR

Weber, 1998

Case retrieval

HVAC system

Tests air conditioning systems

Watson, 2000

Diagnosis and solutions to HVAC maintenanceOperated by salespersons Western AustraliaThe Auguste

ProjectCBR is used to decide whether a patient benefits from a drug and RBR decides which drug to choose

Marling 2001

Planning ongoing care for AD (Alzheimer) cases based on strategies that worked better in past cases

HICAP Case-based planning Munoz Avila 1999

Combines case-based planning with methods in planning NEO’s

PRUDENTIA Jurisprudence research; textual CBR

Weber, 1998

Case retrieval

FormTool CBR in color matching Cheetham GE CRD Savings of 2.25 million per year in productivity and cost reduction

DUBLET Recommends rental properties from different online sources

Hurley, Wilson 2001

Is used on the web and in mobile phonesEmploys Information Extraction tools to gather info from the web- returns properties ranked according to similarity

PTV (personalized TV listings)

Each user receives a daily personalized TV listing specially compiled to suit each user’s individual preferences

Cotter & Smyth

Cbr and collaborative filteringCF makes a recommendation to a person because his or her profile is similar to other people who have chosen the recommended item.

  Recent applications   Springer series on CBR Research and Development

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Ontologies

A hierarchical model of domain knowledge where concepts are organized according to their commonalities and meaning

Embedds knowledge about inter-relations between concepts (e.g., subsumptions) and their properties, plus axioms and rules.

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What are ontologies (in AI)?

general viewa formalism that represents shared conceptualizations and their interrelations in a domain (or subdomain) using a common vocabulary

“Ontologies are explicit specifications of conceptualizations.” most cited definition from Gruber (1993)

specific viewan ontology is an explicit description of:

concepts (or classes) in a domainproperties of each concept describing various features and attributesand restrictions on the attributes (facets)

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Shared, Explicit, and Conceptual

consensual knowledgenot private to one individual, accepted by a group

types and constraints are explicitly defined

conceptual (abstract) model of a domain through its relevant concepts

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Taxonomies1. The classification of organisms in an ordered system that indicates natural relationships. 2. The science, laws, or principles of classification; systematics. 3. Division into ordered groups or categories: “Scholars have been laboring to develop a taxonomy of young killers” (American Heritage)

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Level 1

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E N V I R O N M E N T

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Level 2

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O P E R A T I O N S

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O p era tio ns

Level 2

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T I M E

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T im e

Level 2

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R E S O U R C E S

T ran sp or ta tion W e ap o nsm issi le s, g un s , b om b s, m u n itio ns

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Level 2

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F I E L D S

actio verb action complement

applicable action conditions

sugg verb sugg complement

suggestion originating event

Fields

Level 2

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t ra ns p ortat io n res o u rces

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Level 3

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Level 3

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