2.ModelBased Reflex Agent Intro

8/3/2019 2.ModelBased Reflex Agent Intro

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COMP 4640

Intelligent & Interactive Systems

Programs Supporting

Model - Based Reflex Agents

November 2008

Dr. Cheryl Seals


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Simple reflex agents


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Programs that support

Model - based Reflex Agents

Simple reflex agents

select precepts based on the

current perceptignoring the rest of the precepthistory

Example: Beetle


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Model-based reflex agents


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Programs that support

Model - based Reflex Agents

Most systems are based on condition-action rules

(i.e. situation-action rules, productions, or if-then rules)

(e.g. If car-in-front is braking then initiate-braking p46)

Model-Based Reflex Agents

Most effective way to keep track of the part of the

world it cant see now.

Maintain some internal state that depends onpercept history and thereby reflects at least someof the unobserved aspects of the current state (e.g.

using some type of variable).


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Production Based SystemsThe production rule paradigm originated in the field of AI with theexpert systems rule languages such as OPS5 (Brownston et al. 1985) condition action

An inference engine cycles through all the rules in the systemmatching the condition parts of the rules with data in working memory.

Of all the rules that match (the candidate set), one is selected usingsome conflict resolution policy and this selected rule is fired, that is,its action part is executed.

The action part may modify the working memory, possibly accordingto the matched data and the cycle continues until no more rulesmatch.

Rule based Rules have special ops:

Fire, which causes a rule to be triggeredEnable, which causes a rule to be activatedDisable, which causes a rule to be deactivated

Conflict resolution Break ties with Specification, Sequencing, Meta rules


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Production Based SystemsCLIPS

(CLanguage Integration Production System) Production system developed at NASAs Johnson space

center. Written in ANSI C instead of LISP CLIPS implements standard forward-chaining pattern-

matching algorithm CLIPS knowledge representation similar to OPS5 and ART

systems. Constructs

simple string fact assertion & retractionTemplatesIf-then rules (productions)Objects and instances

NASA uses clips in the following projects

Intelligent computer aided crew training, weatherforecasting, shuttle space planning, shuttlediagnostics, Mission Control Center (telemetry dataanalysis and diagnostics), flight assistance andcontrol

ART commercial expert system has many of thesame features as CLIPS
http://portal.acm.org/author_page.cfm?id=81100007064&coll=GUIDE&dl=GUIDE&trk=0&CFID=9450350&CFTOKEN=41813484http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00176973http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00176973http://portal.acm.org/author_page.cfm?id=81100007064&coll=GUIDE&dl=GUIDE&trk=0&CFID=9450350&CFTOKEN=41813484


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Agent Based Systems

Systems to investigate

Stagecast CreatorTM (www.stagecast.com)

AgentsheetsTM (www.agentsheets.com)


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End User Programming with agentsStagecast Study Report:

We are attempting to create a cross-generational webbased learning community for middle school students,teachers, and seniors.

Learning community will design, construct, and discusssimulations of community issues.

Summary of results of formative evaluation with studentscreating simulation projects.

Proceedings of IEEE Visual Languages 2001, Rosson, Seals2001; CHI 2001; DIS 2002; NSF Research: NSF ITR 0091102.


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Based on a movie metaphorProgramming is facilitated by macro recorder to allowprogramming by demonstration

Behaviors are represented as a set of as a set of

productions or if-then rules

Stagecast Creator


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Procedure

Participants: 10 middle school students

Background survey

Performed in usability testing lab study with think aloud

protocol

Recorded critical incidents

Captured video, audio, and screen

Subjective questionnaire, knowledge survey,retrospective interview


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Visual Agent Programming

Spatial context and visual appearance arerequired elements in a rules precondition

Correct position and appearance are

preconditions for rules

If Precondition is satisfied, Then rule is fired.

Characters may have many instantiations


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Observations and Results

Duration 30-55 minutes Activity I

Duration 34-47 minutes Activity II

Most students were successful in modifying simulationsand adding new characters.

Usability satisfaction

Easy and fun to use

Would like to use it in their classes But needed more exposure to feel confident

No problems with drawing tools

Problems with tools for rule creation


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Issue Likely Cause

Directing input to the wrongwindow Too many similar-lookingwindows

Confused between rules andrule-actions lists

Lists that look similar but havedifferent meanings

Select wrong icon Multiple similar iconsInability to find rules or othercontent in window

Non-traditional method ofscrolling

Misunderstand spotlight andconcept of stretching it

Spotlight metaphor is notobvious or intuitive

Stagecast Usability Problems


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Practical metaphors for icons

Bigger Icons

Fewer layers of scaffoldingRelation between internal variables andvisual state of the simulation.

Role of visual context in rules Rules must match exact visual context, most

PBD system make rules to specific to be reused

Visual Programming Challenges


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End User Programming with agentsAgentSheets Study Report:

AgentSheets is a production basedvisual programming language whereend users create with directmanipulation techniques

Reports a study of teachers learningto build educational simulations ascurricula aids.

Summary of results of formative

evaluation to design agent basedproduction system for end usercreation of educational simulations.

Proceedings of IEEE VisualLanguages 2002, Seals 2002.


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Empirical Study Results

Need robust drawing tools

Objects should be important, not their spatiallocation

Flexible object size

Support for import of objects

Allow incremental testing

Increase the level of usability for noviceprogrammers

Platform independent implementation

Documents

2.ModelBased Reflex Agent Intro