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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 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

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  1. Agent-based Composition of Behavior Models Katia Sycara (PI) Start date: 10/02/02 Gita Sukthankar Anupriya Ankolekar The Robotics InstituteCarnegie Mellon University

  2. Talk Outline • Vision • Limitations of Current Models • Research Objectives • Research Approach • Expected Impact • Accomplishments • Deliverables

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. Functional Architecture Trainer Reasoner Plan Editor Internal Events Knowledge Structures Reasoner Belief Editor CGF Model Trainee Simulation Environment

  9. 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)

  10. 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

  11. 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

  12. 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 …

  13. 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

  14. 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

  15. 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++)

  16. 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

  17. Show entities information SAF Manager SAF entities Show existing OTB simulation Show network information Show Current PDUs in OTB Show UT entities

  18. 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

  19. 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)

  20. 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.

  21. 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 1 2 3 4 http://www.millenniumsend.com/user/pender/articles/hands.html

  22. A B C D Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  23. A B C D Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  24. A C D B Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  25. A C D B Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  26. C D A B Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  27. C D A B Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  28. C D A B Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  29. C D A B Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  30. C D B A Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  31. C D A B Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  32. C D A B Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  33. C D A B Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  34. C D B A Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  35. C D B A Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  36. C D B A Composition: L-Shaped Corridor and Room Clearing MCWP 3-35.3 Military Operations on Urbanized Terrain (MOUT)

  37. Composition: L-shaped Corridor + Stacked 2-Man Room Clearing

  38. 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

  39. 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

  40. 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

  41. Hand Signal Behaviors

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