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Agent-based Composition of Behavior Models. Katia Sycara (PI) Start date: 10/02/02 Gita Sukthankar Anupriya Ankolekar The Robotics Institute Carnegie Mellon University. Talk Outline. Vision Limitations of Current Models Research Objectives Research Approach Expected Impact

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agent based composition of behavior models

Agent-based Composition of Behavior Models

Katia Sycara (PI) Start date: 10/02/02

Gita Sukthankar

Anupriya Ankolekar

The Robotics InstituteCarnegie Mellon University

talk outline
Talk Outline
  • Vision
  • Limitations of Current Models
  • Research Objectives
  • Research Approach
  • Expected Impact
  • Accomplishments
  • Deliverables
slide3
Fully automated, high fidelity Computer Generated Forces have enormous value for military simulation and training
  • High fidelity CGFs provide realistic adversaries and team mates
    • Utilize multi-agent architectures to go beyond current limited behaviors to adaptive opponent/teammates with human-like unpredictability
    • Can learn from experience
    • Embodying Team behaviors
  • Can be used for shipboard and embedded training
    • Training can be conducted using standard computer equipment (e.g. PCs)
  • Will be cost-effective and affordable
    • Automated CFGs reduce the training manning requirements
    • Agent-oriented software engineering techniques promote modularity and reuse
limitations of current models
Limitations of Current Models
  • Current CGF training models are limited and inflexible
    • They exhibit a small hard-coded set of behaviors
    • They do not allow the coach to easily customise the training experience
    • They are hard to develop and troubleshoot
  • Current human performance modeling techniques
    • Have not been successfully scaled to complex tasks
    • Have not been applied to modeling teams
    • Models are expensive to construct
    • Models do not allow reuse
research objectives
Research Objectives
  • Develop techniques that:
    • Enable CGFs to increase range of behaviors to incorporate smart human-like strategies and adaptation
    • Allow efficient reuse and composition of CGF models
    • Allow the development of models of adversaries and team mates that are consistent with human behavior modeling
    • Reduce model construction time and cost
research approach
Research Approach

Integration of multi-agent architectures and software engineering techniques to increase CGF sophistication and enable reuse

  • Leverage our expertise in the development of intelligent agents to increase the autonomy, range of behaviors and long-term strategic level thinking of the CGFs
    • Use knowledge bases of composable CGF plan fragments that encapsulate particular behaviors
  • Use libraries of reusable software components and connectors to create executable code
  • COTS game engines and state of the art animations provide a realistic and affordable simulation platform deployable for classroom, shipboard, and embedded training (PC’s with game software)
  • Demonstration Domain: Urban Warfare
what s unique about our approach
What’s unique about our approach?
  • The combination of semantically rich agent representation and software engineering development methodology
  • The multi-agent architectural approach enables modeling of team behaviors
  • This approach will result in affordable, coachable teams of realistic training forces
functional architecture
Functional Architecture

Trainer

Reasoner

Plan Editor

Internal

Events

Knowledge

Structures

Reasoner

Belief Editor

CGF Model

Trainee

Simulation Environment

armies fight in teams and so must their training simulations
Armies Fight in Teams and so must their Training Simulations
  • Teamwork in Open Environments [Sycara et al.] incorporates heterogeneous teams and dynamic team formation
    • Teams are not assumed to be fixed in size or team members abilities
    • Model accommodates dynamic role assignment according to current situation and individual capability
    • Model accommodates discovery and incorporation into the team of new appropriate team members (adapts to the loss of members)
    • Teams can be formed/reformed dynamically during execution in response to incoming/changing goals and environment
    • Negotiation of team goals and commitments
    • Has been applied to Joint Mission Planning (Agent Storm)
our approach enables reuse at multiple levels
Our approach enables reuse at multiple levels
  • Individual CGFs can be adapted for different scenarios and domains
  • Programmers reuse already developed CGF behavior fragments to construct new CGFs
  • Our multi-agent architecture (RETSINA) is a proven model of software development that has been reused across multiple domains
composition
Composition

Composition of agents at task level

  • SE language: an agent is a computational process (an “smart” component). An agent can be viewed as a unit of planning and execution
  • Thus, composition of plan fragments and associated code
    • Manage interdependencies between plan fragments by matching preconditions, beliefs, commitments, constraints (at reactive and cognitive levels)
    • Manage interdependencies between code by matching inputs and outputs

Promising approach from Software Engineering

  • Use a library of adapters and connectors to manage interdependencies and repair violated dependencies between composed agents
appropriate representation facilitates reuse and composition of pre existing plans

Explored room

Defeated enemies

Team formed

Unexplored room

Appropriate representation facilitates reuse and composition of pre-existing plans

Knowledge base of

pre-developed plan fragments

CLEAR

AREA x

CLEAR

INTERIOR

OF x

GAIN

DOMINANT

POSITION

CLEAR

ENTRY

IF DOOR LOCKED

SHOOT BOLT

IF DOOR CLOSED

KICK DOOR

IF WIDE ENTRY

STRAFE ENTRY

Abstract plan fragments

HUG

WALL

Executable actions communicated to UT and executed by CGF

appropriate representation facilitates reuse and composition of pre existing plans13

Team formed

Unexplored room

Exploredroom

Defeated enemies

DOOR LOCKED:

SHOOT BOLT

HUG

WALL

Abstract plan fragments

Executable actions that are communicated to UT and executed by CGF

Appropriate representation facilitates reuse and composition of pre-existing plans

CLEAR

BUILDING

Clearing Room

CLEAR

BUILDING

INTERIOR

CLEAR

ROOM

CLEAR

ROOM

INTERIOR

GAIN

DOMINANT

POSITION

CLEAR

ENTRY

Plan fragment reuse and composition in similar new situations

appropriate representation facilitates reuse and composition of pre existing plans14

Team formed

Unexplored cave

Explored cave

Defeated enemies

WIDE ENTRY:

STRAFE ENTRY

HUG

WALL

Abstract plan fragments

Executable actions that are communicated to UT and executed by CGF

Appropriate representation facilitates reuse and composition of pre-existing plans

CLEAR

CAVE

CLEAR

CAVE

INTERIOR

GAIN

DOMINANT

POSITION

CLEAR

ENTRY

Clearing Cave

Plan fragment reuse and composition in similar new situations

realistic and affordable simulation environment unrealtournament ut

CGF

Agent

CGF

Agent

CGF

Agent

CGF

Agent

Realistic and Affordable Simulation Environment: UnrealTournament (UT)

Gamebots TCP/IP Interface

Urban Scenario

UT Engine (C/C++)

we can embed cgfs into larger tactical simulations
We can embed CGFs into larger tactical simulations

UT Game Engine

To provide real-time high quality graphics and detailed local behavior

OneSAF

To simulate larger military entities, behaviors, & capabilities

Correlated

entities

Correlated

terrain

saf manager

Show entities information

SAF Manager

SAF entities

Show existing OTB simulation

Show network information

Show Current PDUs in OTB

Show UT entities

advantages of our approach
Advantages of our Approach
  • Reuse
    • knowledge base of plan fragments and beliefs supports reuse in new situations
  • Modularity
    • agent-based architecture provides modularity of CGF plans and behaviors
  • Composition
    • matching algorithms enable the matching of plan fragments and behaviors so they can be composed to form more intelligent adversaries and team mates, as situations warrant
  • Verification
    • our representation formalism can be used for formal model-checking and verification of desirable properties of the software, thus reducing development time
expected impact
Expected Impact

If successful, our research will provide Reprogrammable and Instructable CGF teams which:

  • Can be “Coached” by training instructor using a simple GUI to provide trainee appropriate combat experiences
  • Exhibit realistic team behaviors
  • Considerably reduce development time and cost while increasing behavior realism
  • Can be embedded in larger simulations (e.g. OneSAF)
accomplishments
Accomplishments
  • Developed initial Agent Representation Scheme
  • Developed initial algorithm that matches current situation to previously developed plan fragments for reuse.
  • Implemented initial teamwork scenario in Unreal Tournament.
  • Publications:
    • Sycara, K. et al. “Integrating Agents into Human Teams”, In Salas E. (ed.) Team Cognition, Erlbaum Publishers, 2003. In Press.
    • Sycara K. et al. “Ontologies in Agent Architectures”, In S. Staab and R. Studer (eds.) Handbook on Ontologies in Information Systems, Springer 2003. In Press.
hand signal behaviors
Hand Signal Behaviors
  • Hand signals are important for team communication in urban warfare since the enemy is often in close proximity.
  • Extensions to Gamebots allow AI control over these new behaviors.

Cover Area

Listen

Wait

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http://www.millenniumsend.com/user/pender/articles/hands.html

composition l shaped corridor and room clearing

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B

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Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing23

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Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing24

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Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing25

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Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing26

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Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing27

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Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing28

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Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing29

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Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing30

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Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing31

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B

Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing32

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D

A

B

Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing33

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A

B

Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing34

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A

Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing35

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A

Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

composition l shaped corridor and room clearing36

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B

A

Composition: L-Shaped Corridor and Room Clearing

MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

milestones and deliverables
Milestones and Deliverables

4/30/03- 9/30/03

  • Develop initial scenarios for CGF deployment
  • Develop initial agent teamwork representation
  • Implement the initial scenarios in Unreal Tournament

10/01/03-12/30/03

  • Evaluate the resulting CGFs for realism
  • Refine teamwork representation as a result

1/01/04 – 3/30/04

  • Develop techniques for agent behavior reuse
  • Continue development and testing of teamwork schemes
  • Implement them and test them in new situations
milestones and deliverables 2
Milestones and Deliverables (2)

4/01/04- 6/30/04

  • Evaluate the resulting CGFs from previous quarter for realism and ease of development
  • Develop and test mechanisms for agent behavior composition

7/01/04 – 9/30/04

  • Develop techniques for resolution of mismatches in agent descriptions
  • Develop techniques for propagation of constraints across plans and agent beliefs

10/01/04 – 12/30/04

  • Implement techniques from previous quarter in Unreal Tournament and test in new situations
  • Develop techniques for belief propagation across team members
milestones and deliverables 3
Milestones and Deliverables (3)

1/01/05- 3/30/05

  • Develop indexing scheme for agent behaviors
  • Develop techniques for dynamic retrieval of agent behaviors and reuse

4/01/05 – 6/30/05

  • Implement dynamic retrieval and reuse of agent behaviors in new situations
  • Design and implement coach’s GUI

7/01/05 – 9/30/05

  • Test control of CGFs from coach’s GUI
  • Demonstrate embedding of CGFs in OneSAF