COMP 4640
Intelligent & Interactive Systems
Programs Supporting
Model - Based Reflex Agents
November 2008
Dr. Cheryl Seals
Simple reflex agents
Programs that support
Model - based Reflex Agents
Simple reflex agents
select precepts based on the
current percept
ignoring the rest of the precept
Example: Beetle
Model-based reflex agents
Programs that support
Model - based Reflex Agents
Most systems are based on “conditionaction” 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 can’t 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).
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
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
Production Based Systems
(“C”Language Integration Production System)
Production system developed at NASA’s Johnson space
Written in ANSI C instead of LISP
CLIPS implements standard forward-chaining patternmatching algorithm
CLIPS knowledge representation similar to OPS5 and ART
simple string fact assertion & retraction
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
ART commercial expert system has many of the
same features as CLIPS
Agent Based Systems
Systems to investigate
 Stagecast CreatorTM (
 AgentsheetsTM (
End User Programming with agents
Stagecast 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.
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
Participants: 10 middle school students
Background survey
Performed in usability testing lab study with “think aloud”
Recorded critical incidents
Captured video, audio, and screen
Subjective questionnaire, knowledge survey,
retrospective interview
Visual Agent Programming
Spatial context and visual appearance are
required elements in a rule’s precondition
Correct position and appearance are
preconditions for rules
Characters may have many instantiations
If Precondition is satisfied, Then rule is fired.
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
Stagecast Usability Problems
Likely Cause
Directing input to the wrong
Too many similar-looking
Confused between rules and
rule-actions lists
Lists that look similar but have
different meanings
Select wrong icon
Multiple similar icons
Inability to find rules or other
content in window
Non-traditional method of
Misunderstand spotlight and
concept of stretching it
Spotlight metaphor is not
obvious or intuitive
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
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.
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
Empirical Study Results
Need robust drawing tools
Objects should be important, not their spatial
Flexible object size
Support for import of objects
Allow incremental testing
Increase the level of usability for novice
Platform independent implementation

Programs that support Model