Transcript AI For OOTW - The OR Society

AI for OOTW
Representing
Plausible Behaviour in
OOTW Simulators
© HVR Consulting Services Ltd
Steve Curram
Mirek Gliniecki
Mark Cusack
Nick Exon
Modelling and Analysis Group
HVR Consulting Services Ltd
Selborne House, Mill Lane
Alton, Hampshire
GU34 2QJ
[email protected]
Distributed Technologies Group
QinetiQ Ltd
St Andrews Road
Malvern, Worcestershire
WR14 3PS
[email protected]
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Background
Purpose of overall study is to create
simulators that allow stress testing of
Command Information Systems and the
people who use them in an OOTW situation
Features:

Multiple factions and agencies
 Highly political environment
 Restrictive RoE
 Media coverage and public opinion
 Multiple information sources
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Background (2)

Purpose of simulators for use by commanders is to
provide a challenging and thought provoking
environment

The simulators are not designed to be analysis tools

They will conform to the High Level Architecture
(HLA) using QinetiQ’s BeanXS environment

Use Java and JavaBeans™ technology

AI is only one component of the study

Quick and cheap to set up
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Study Approach

Identification of behaviour types and requirements
of AI for OOTW simulators

Review of technology and case studies to generate
shortlist of approaches for further investigation

Develop stand-alone AI objects for analysis

Test AI objects in an integrated environment

Prototype in a small simulation model
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Aims of the AI

Provide plausible behaviour in simulation
from a player’s perspective
– computer generated actors
– occurrence of events

Give player something to think about

Be responsive to player’s actions

Not to be too transparent - player should be
immersed in environment rather than trying
to beat the AI
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It’s Artificial Intelligence, BUT
It is more important that behaviour appears
plausible to a player rather than for the
mechanism to conform to any psychological
theories
 The AI should be reasonably quick and easy
to set up
 Probably more akin to the requirements for
commercial computer games than traditional
military models

Soft AI
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Approaches Considered

Random Sampling - does not take account of
conditions, though not entirely predictable - could be
combined with other approaches
 Knowledge-Based Systems (& related) - can be too
predictable if rules are simple, while ease of setting up
falls rapidly as rules become more complex
 Fuzzy Logic - rules are generally simpler but more
powerful than KBSs, needs some experience in choice
of fuzzy regions, linguistic terms can help trainers in
tweaking scenarios
 Case-Based Reasoning - full systems can be time
consuming to set up and have overhead of a shell,
simpler versions may be useful for recognising when
scripted events can occur
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Approaches Considered (2)

Finite State Machines - commonly used in computer
games, simple to set up, can get predictable for player
 Fuzzy State Machines - adaptation of Finite State
Machines using fuzzy rules, becoming popular in
computer games, membership functions could be used
for random sampling
 Bayesian Belief Networks - powerful representation of
factors on behaviour, can be difficult to set up, difficulty
of validation
 Neural Networks (various types) - adaptive, powerful
representation but time consuming and difficult to set
up and validate
 Genetic Algorithms - adaptive, but too slow for
interactive simulation
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Short-listed Approaches

Finite State Machine (FSM)
– for simple behaviour
– create generic FSM object to which states and
transitions are added
– easy to set up
Patrol
Alert
START STATE
Patrol
Patrol
Patrol
Alert
Retreat
Combat
Combat
TRANSITION
CONDITION
Sound Heard
Armed Unit Spotted
Fired On
Unit Recognised &
Attitude Hostile &
Unit Outnumbered
Significantly
Outnumbered
END STATE
Alert
Alert
Combat
Combat
Retreat
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Short-listed Approaches

Fuzzy State Machine (FuSM)
–
–
–
–
for more complex behaviour
linguistic or numerical inputs
smoother transitions between states
create generic FuSM to which terms and fuzzy rules
can be added
– state can be selected from centre of gravity or using
random sampling from fuzzy region
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Implementation Considerations

Classes developed in Java and converted to
JavaBeans™

Will be used in simulations along with other
JavaBeans™

They need to pick up messages and hold their
own state

Need to respond to a request or simulation time

Behaviour should be able to be time-scaleable
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Fuzzy State Machine

Fuzzy State Machine and test
rig (shown) developed
 Allows absolute rules outcome is state membership
values
 Allows change rules outcome is amount of change
to current state value
 States and rules can be readin using a scripting language
 JavaBean version developed
 Wrapper/Interpreter allows
FuSMBean to interact with
rest of simulation
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Behaviour Type - Attitude

Attitude of an actor towards other actors
– may differ at different levels, e.g. faction leaders
towards UN, or faction soldier towards UN
– use Finite State Machine or Fuzzy State Machine
Hostile
Current
Attitude
Mistrustful
Mistrustful
UN Sanctions
Against
Faction
Neutral
Neutral
Ground Capture
Incidents of
UN Protection
Sympathetic
Incidents of
UN Counteraction
Friendly
Freedom of
Movement
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Behaviour Type - Negotiation

Negotiation between actors
– manual: player has role as a mediator, and
suggests compromise solutions
– automatic: compromise solutions based on
global approach to negotiation settings
– response of actors depends on the weighted
distance from their ideal solution and their
attitude towards the negotiations
– makes use of Fuzzy State Machines
– likely to be iterative process over time (may
be short or long periods)
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Behaviour Type - Negotiation
Perceived
Threat from
Enemy Faction
Faction
Attitude to UN
Perceived UN
Confrontation
Level
Perceived Attitude
of Civilian
Population
Attitude to Negotiation
Intransigent
Resistant
Neutral
Supportive
Compliant
Distance On Issues
Positions on Issues
Land Allocation
Governmental Power
Military Power
Economic Freedom
Financial/Infrastructure Aid
UN Retained Presence
Law Actions Against Pol/Mil Leaders
Weighted Difference
Between Desired and
Offered Positions
Relative Weighting
of Issues
Closeness of Proposal
to Desired Outcome
(Fuzzy Set)
Degree of
Agreement
Offered Positions
on Issues
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Vignette Development (WIP)
Two warring factions
 Refugees from both population groups
 Refugee camps for both groups

– populations, food, medicine, death rates
UN convoys supplying camps
 Road blocks run by militia factions
 Media coverage
 Includes: crowd movement, terrain, pathfinding, attitude, negotiation

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Observations So Far
Generic and flexible FSM and FuSMs can
be created and converted to JavaBeans™
 Wrappers control interaction with other
JavaBeans™ in the simulation
 Specific behaviour loaded in from XML
 Types of behaviour can be stored in
libraries
 Experimenting with messaging
 Refining rules - want sufficient power with
simplest set possible

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Questions ?
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