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Folk Psychology for Human Modelling. Extending the BDI Paradigm. Emma Norling Department of Computer Science and Software Engineering The University of Melbourne. Outline. Human Modelling, Folk Psychology and BDI Extending BDI for Human Modelling Perception and action Decision making

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folk psychology for human modelling

Folk Psychology forHuman Modelling

Extending the BDI Paradigm

Emma NorlingDepartment of Computer Science and Software EngineeringThe University of Melbourne

outline
Outline
  • Human Modelling, Folk Psychology and BDI
  • Extending BDI for Human Modelling
    • Perception and action
    • Decision making
  • Conclusions & future work
human modelling
Human Modelling
  • Human modelling is the simulation of human behaviour
  • Includes applications such as
    • Operations analysis
    • Human-in-the-loop training
    • Entertainment
  • Focus is on “holistic” behaviour
folk psychology
Folk Psychology
  • Folk psychology is ‘layman’s psychology’— a means of explaining the behaviour of others via their reasoning
  • It does not attempt to capture how the mind really works
  • It is a robust mechanism for explaining and predicting the behaviours of others
  • It is also commonly used to explain one’s own behaviour
the bdi agent architecture

Goals

Plans

Reasoner

Beliefs

Intentions

The BDI Agent Architecture

Sensors

AGENT

Actions

  • Based in folk psychology – Dennett’s intentional stance
  • The agent will intend to do what it believes will achieve its goals
bdi and human modelling
BDI and Human Modelling
  • BDI has been used with considerable success for human modelling
  • The success is largely due to its folk psychological roots
    • Facilitates knowledge representation
    • Agents’ behaviour can be understood by laymen
  • However…
bdi and human modelling1
BDI and Human Modelling
  • The intentional stance is a high level abstraction of human reasoning
  • It ignores many generic aspects of human behaviour, such as emotion, fatigue, mental and physical limitations
  • More of these should be included in a framework designed for human modelling
a bdi based human modelling framework
A BDI-BasedHuman Modelling Framework
  • Given that BDI has had success in human modelling, can it be extended to be better-suited to this purpose?
  • Proposed approach: integrate folk psychological explanations where applicable, attach modules otherwise
an attached module

An attached module

Perception and action

attaching a module
Attaching a Module
  • Vision and motion operates at such a low level that folk psychology is not typically used to describe them
  • Vision module feeds inputs to the agent’s beliefs; action module introduces timing and errors to the agent’s action
slide12

Intentions

Move eye

Move mouse

AGENT

Follow instructions

Dial number

Beliefs

Press “5”

Phone numbers

Desires

Mouse position

Button positions

an integrated explanation

An Integrated Explanation

Recognition Primed Decision-making (RPD)

an integrated explanation1
An Integrated Explanation
  • BDI uses utility-based decision-making, or selects first applicable, but humans use a wide range of decision strategies
  • In certain types of domain, RPD can account for up to 95% of the choices
  • RPD is a descriptive model of decision making, using folk psychological concepts
slide15

Experience the situation in a changing context

Experience the situation in a changing context

No

No

Diagnose

Diagnose

Is the situation typical?

Is the situation typical?

Moredataneeded

Moredataneeded

Yes

Clarify

Clarify

Four by-products of recognition:

Anomaly

Expectancies

Expectancies

Relevant cues

Relevant cues

Anomaly

Plausible goals

Plausible goals

Possible actions

Possible actions

Yes, but

Yes, but

Evaluate selected action:

Will it work?

Evaluate selected action:

Will it work?

Modify

Modify

No

No

Changing beliefs

Applicable plans

Plan selection

Plan failure

Intention

Yes

Implement course of action

Implement course of action

Recognition-Primed Decision Making

From Sources of Power by G. Klein (1998)

implementation
Implementation
  • Agent must be able to learn to recognise situations in order to make decisions
  • BDI agents use plan context to limit the plans considered, but this is static
  • Use meta-level reasoning to enable Q-learning based plan selection
    • Update Q-values on plan completion
exploration of parameters
Exploration of Parameters

Q(st, at) ← Q(st, at) + α[rt+1 + γmax Q(st+1, a) Q(st, at)]

a bigger problem quake 2
A Bigger Problem: Quake 2
  • BDI-based agents to play Quake 2
    • Connect to the game server as if they were a player client
    • Receive the same data as the player client, send commands via simulated key presses and mouse actions
    • Model expert Quake 2 players
  • It doesn’t work!
  • (But not because of the extension)
conclusions
Conclusions
  • To extend the BDI framework to better suit human modelling:
    • Include more of the generic aspects of human behaviour/reasoning in the framework
    • Maintain the folk psychological roots, because this is its greatest strength
  • Integrate folk psychological explanations where they exist
  • Use low-level explanations that do not impact on the knowledge representation otherwise
future work
Future Work
  • COJACK – an extension to JACK enabling cognitively plausible models of human variability
  • Social aspects of agency
    • Can folk psychology be used to capture social intelligence?