COMP 4640 Intelligent & Interactive Systems

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COMP 4640Intelligent 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 percept
• ignoring the rest of the precept history
• 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 on
  percept history and thereby reflects at least
  some of the unobserved aspects of the current
  state (e.g. using some type of variable).

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Production Based Systems

• The production rule paradigm originated in the
  field of AI with the expert systems rule
  languages such as OPS5 (Brownston et al. 1985)
• condition ? action
• An inference engine cycles through all the rules
  in the system matching the condition parts of the
  rules with data in working memory.
• Of all the rules that match (the candidate set),
  one is selected using some 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 according to the matched data and the
  cycle continues until no more rules match.
• Rule based
• Rules have special ops
• Fire, which causes a rule to be triggered
• Enable, which causes a rule to be activated
• Disable, which causes a rule to be deactivated
• Conflict resolution
• Break ties with Specification, Sequencing, Meta
  rules

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Production Based Systems

• CLIPS
• (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 retraction
• Templates
• If-then rules (productions)
• Objects and instances
• NASA uses clips in the following projects
• Intelligent computer aided crew training, weather
  forecasting, shuttle space planning, shuttle
  diagnostics, Mission Control Center (telemetry
  data analysis and diagnostics), flight assistance
  and control
• ART commercial expert system has many of the same
  features as CLIPS

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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
  web based learning community for middle school
  students, teachers, and seniors.
• Learning community will design, construct, and
  discuss simulations of community issues.
• Summary of results of formative evaluation with
  students creating simulation projects.
• Proceedings of IEEE Visual Languages 2001,
  Rosson, Seals 2001 CHI 2001 DIS 2002 NSF
  Research NSF ITR 0091102.

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Stagecast Creator

• Based on a movie metaphor
• Programming is facilitated by macro recorder to
  allow programming by demonstration
• Behaviors are represented as a set of as a set of
  productions or if-then rules

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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 are
  required elements in a rules precondition
• Correct position and appearance are preconditions
  for rules

Characters may have many instantiations
If Precondition is satisfied, Then rule is fired.
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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
  simulations and 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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Stagecast Usability Problems
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Visual Programming Challenges

• Practical metaphors for icons
• Bigger Icons
• Fewer layers of scaffolding
• Relation between internal variables and visual
  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

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End User Programming with agents AgentSheets
Study Report

• AgentSheets is a production based visual
  programming language where end users create with
  direct manipulation techniques
• Reports a study of teachers learning to build
  educational simulations as curricula aids.
• Summary of results of formative evaluation to
  design agent based production system for end user
  creation of educational simulations.
• Proceedings of IEEE Visual Languages 2002, Seals
  2002.

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Example Rule
- left-hand specifies a before state -
right-hand specifies one or more actions to take
if state is confirmed - multiple rules are
tested in order, first match fires
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Empirical Study Results

• Need robust drawing tools
• Objects should be important, not their spatial
  location
• Flexible object size
• Support for import of objects
• Allow incremental testing
• Increase the level of usability for novice
  programmers
• Platform independent implementation