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Objectives
Introduce theory-based models for predicting human performance
Introduce competence-based models for assessing cognitive activity
Relate modelling to interactive systems design and evaluation
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Some Background Reading
Dix, A et al., 1998, Human-Computer Interaction (chapters 6 and 7) London: Prentice Hall
Anderson, J.R., 1983, The Architecture of Cognition, Harvard, MA: Harvard University Press
Card, S.K. et al., 1983, The Psychology of Human-Computer Interaction, Hillsdale, NJ: LEA
Carroll, J., 2003, HCI Models, Theories and Frameworks: towards a multidisciplinary science, (chapters 1, 3, 4, 5) San Francisco, CA: Morgan Kaufman
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Task Models
Researcher’s Model of User, in terms of tasks
Describe typical activities
Reduce activities to generic sequences
Provide basis for design
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Pros and Cons of Modelling
PROS Consistent description through (semi) formal
representations Set of ‘typical’ examples Allows prediction / description of performance
CONS Selective (some things don’t fit into models) Assumption of invariability Misses creative, flexible, non-standard activity
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Generic Model Process?
Define system: {goals, activity, tasks, entities, parameters}
Abstract to semantic level Define syntax / representation Define interaction Check for consistency and completeness Predict / describe performance Evaluate results Modify model
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Hierarchical Task Analysis
Activity assumed to consist of TASKS performed in pursuit of GOALS
Goals can be broken into SUBGOALS, which can be broken into tasks
Hierarchy (Tree) description
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Hierarchical Task Description
1 .0S w itch on O H P
2 .0C h eck p ro jec tion
3 .0P lace fo il on O H P
4 .0F ocu s p ro jec tion
0 .0P resen t O H P s lid es
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The “Analysis” comes from plans
PLANS = conditions for combining tasks Fixed Sequence
P0: 1 > 2 > exit Contingent Fixed Sequence
P1: 1 > when state X achieved > 2 > exit P1.1: 1.1 > 1.2 > wait for X time > 1.3 > exit
Decision P2: 1 > 2 > If condition X then 3, elseif
condition Y then 4 > 5 > exit
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Performance vs. Competence
Performance Models Make statements and predictions about
the time, effort or likelihood of error when performing specific tasks;
Competence Models Make statements about what a given
user knows and how this knowledge might be organised.
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Production Systems
Rules = (Procedural) Knowledge
Working memory = state of the world
Control strategies = way of applying knowledge
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The Problem of Control
Rules are useless without a useful way to apply them
Need a consistent, reliable, useful way to control the way rules are applied
Different architectures / systems use different control strategies to produce different results
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Production Rules
IF conditionTHEN action
e.g., IF ship is docked
And free-floating shipsTHEN launch shipIF dock is free
And Ship matchesTHEN dock ship
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States, Operators, And Reasoning (SOAR)
http://www.isi.edu/soar/soar.html
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States, Operators, And Reasoning (SOAR)
Sequel of General Problem Solver (Newell and Simon, 1960)
SOAR seeks to apply operators to states within a problem space to achieve a goal.
SOAR assumes that actor uses all available knowledge in problem-solving
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Soar as a Unified Theory of Cognition Intelligence = problem solving +
learning Cognition seen as search in problem
spaces All knowledge is encoded as productions
a single type of knowledge All learning is done by chunking
a single type of learning
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Young, R.M., Ritter, F., Jones, G. 1998 "Online Psychological Soar Tutorial"
available at: http://www.psychology.nottingham.ac.uk/staff/Frank.Ritter/pst/pst-tutorial.html
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SOAR Activity Operators: Transform a state via some action
State: A representation of possible stages of progress in the problem
Problem space: States and operators that can be used to achieve a goal.
Goal: Some desired situation.
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SOAR Activity
Problem solving = applying an Operator to a State in order to move through a Problem Space to reach a Goal.
Impasse = Where an Operator cannot be applied to a State, and so it is not possible to move forward in the Problem Space. This becomes a new problem to be solved.
Soar can learn by storing solutions to past problems as chunks and applying them when it encounters the same problem again
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SOAR ArchitectureChunkingmechanism
Production memory
Pattern ActionPattern ActionPattern Action
Decision procedure
Working memoryManager
Preferences Objects
Conflict stack
Working memory
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Explanation
Working Memory Data for current activity, organized into
objects Production Memory
Contains production rules Chunking mechanism
Collapses successful sequences of operators into chunks for re-use
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3 levels in soar
Symbolic – the programming level Rules programmed into Soar that match
circumstances and perform specific actions Problem space – states & goals
The set of goals, states, operators, and context. Knowledge – embodied in the rules
The knowledge of how to act on the problem/world, how to choose between different operators, and any learned chunks from previous problem solving
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How does it work?
A problem is encoded as a current state and a desired state (goal)
Operators are applied to move from one state to another
There is success if the desired state matches the current state
Operators are proposed by productions, with preferences biasing choices in specific circumstances
Productions fire in parallel
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Impasses
If no operator is proposed, or if there is a tie between operators, or if Soar does not know what to do with an operator, there is an impasse
When there are impasses, Soar sets a new goal (resolve the impasse) and creates a new state
Impasses may be stacked When one impasse is solved, Soar pops up
to the previous goal
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Learning
Learning occurs by chunking the conditions and the actions of the impasses that have been resolved
Chunks can immediately used in further problem-solving behaviour