Outline

1. DARPA CoABS Project:NEO TIEs March 11, 1999 briefingOnn ShehoryCarnegie Mellon UniversityNEO TIEs coordinator: Katia Sycara

2. Outline • Goals of the CoABS project • What NEO TIEs are • How NEO TIEs address the goals • The NEO problem domain • TIEs details: • Participants • Components and interaction • Scientific claims

3. CoABS High-level Goals • Exploit the advantages of agent technology for military goals: • Information gathering and filtering • Mission planning • Execution monitoring • Develop and standardize infrastructure for agent based systems, to support: • Inter multi-agent-system synergy • Integration of legacy systems via “agentification” • Scalability of agent based systems • Services (aka grid services) for agent activity and interoperation

4. What are NEO TIEs? • Non-combatant Evacuation Operation: • Requires information from multiple, multi-modal sources, may change dynamically, be unreliable • Requires time-critical (re-) planning under uncertainty • Collaboration among distributed humans, machines • Plan execution monitoring • Agent Technology Integration Experiments aim at: • Demonstrating agent support for NEO activity by • Exploiting agent technology for information gathering, user interfacing and collaboration • Automating and speeding up critical planning and monitoring • Interoperation with legacy components (e.g. military air lift planner such as CAMPS) for NEO

5. Do NEO TIEs address goals? • The 3 NEO TIEs do • Use existing agents, MAS, services, legacy systems • Make them interoperate: resolve ACL conflicts, build interoperability agents • Enhance human/MAS collaboration/interfacing • Integrate agent and legacy (re-) planning • Demonstrate robustness via agent substitutability • They do not • Evacuate (but may plan for, monitor and display it) • Take the critical decisions (but may advise)

6. Grid by-products of TIEs • Translation (e.g. RETSINA - OAA) • Visualization of multi-agent activity • Support for different types of simulators • Networking infrastructure • Language/tools for use of GRID services • Service description languages (e.g. LARKS)

7. The NEO problem domain • Location: Kuwait city. Time: 2005 • Iraq not a threat, reduced military presence in mideast • Congress in Kuwait city on ecology & oil conservation • Unrest: • Night explosion at the conference center - no casualties • Terrorist group issues threat against attendees, American, etc. • US activity: • Ambassador orders evacuations (smaller numbers) • AMC plans large evacuation • Joint Force Commander involved (plan, secure evacuation routes)

8. TIE 1: Helicopter Evacuation • Technical lead: Milind Tambe (ISI) • Participants: • AVDS (Khosla/Thrun, CMU) • Ariadne (Minton/Knoblock, USC/ISI) • Helicopter agents (Tambe/Shen, USC/ISI) • Quickset (Phil Cohen, OGI) • RETSINA route planner (Sycara/Payne, CMU) • TEAMCORE (Tambe/Shen, USC/ISI)

9. TIE 1: Scenario description • Joint Forces Commander (JFC) uses Quickset to allocate landing zone (LZ) for helicopters in KWI • Route planner plans from safe assembly point to LZ • Helicopter agents simulate transport using MODSAF • Ariadne posts route facts. Route planner is prompted • Quickset displays explosion • Route planner provides new plan • Helicopter agents modify their plans to the alternative • Agent Visualization Database Server (AVDS) shows inter-agent connections

10. TIE 1: Scientific Claims Key Questions 1. Can multi-agents be programmed at the team-level? (Team-Oriented Programming) 2. What are the key requirements for ACLs in team setting? 3. What are the key requirements for distributed monitoring & diagnosis to provide (guaranteed) robustness?

11. Team-Oriented Programming(TOP) Short-Term Goals • Can TIE team be programmed by: • Organization Hierarchy • Team Procedures • Current team goals and plans • Automated coordination via TEAMCORE? Long-Term Goals • Extract general principles of “TOP” and build tools to facilitate “TOP”

12. Distributed Monitoring, Diagnosis & Recovery Short-Term Goals • Explore appropriateness of existing techniques in TEAMCORE for distributed monitoring, diagnosis & recovery • Build primitives for TEAMCORE agents to monitor domain-level agents • Compile logs of failures Long-Term Goals • Extract general principles from compiled logs, add to existing TEAMCORE techniques

13. ACLs for TEAMs Short-Term Goals • Explore shortcomings of existing ACLs for TEAMWORK • Explore interoperation of OAA (Quickset) & TEAMCORE (KQML) Long-Term • Investigate efficiency of semantics of ACL (particularly for teamwork) • Development of semantic interpretation bridge between TEAMCORE and OAA

14. TIE 2: People finder/mover • Technical lead: Steve Minton (ISI) • Participants: • Ariadne (Minton/Knoblock, USC/ISI) • CTF (Paul Cohen, UMASS) • OAA & MMMap (Cheyer/Martin, SRI) • Prodigy & simulator (Veloso/Tucker, CMU) • WebTrader (Pazandak/Bannon, OBJS)

15. TIE 2: Scenario description • JFC uses Assistant agent to order evacuation • OAA Multi-Modal Map (MMM) displays Kuwait city • Ariadne finds number/location of evacuees • WebTrader locates URLs of relevant info. for Ariadne • Prodigy plans routes for gathering evacuees • JFC sets assembly point, displayed by MMM • Evacuees’ locations are displayed on MMM • Assistant agent displays list of unlocated evacuees • Ariadne goes to Geocoder - converts addresses to lat/long • Capture The Flag (CTF) generates traffic obstructions • Ariadne monitors roads, posts status changes, Prodigy replans, MMM re-displays updates

16. Scientific Claims Technical ChallengesTIE 2 1. Coherent Communication • Webtrader brokers information sources for Adriadne, it dynamically incorporates request “Address in Kuwait for conference attendees” • Multiple translators glued together (e.g., speech (MMM)  Menus) 2. Control • Planner controls multiple simulated physical agents • Planner responds to outside events • Posting goals 3. Human in the loop

17. TIE 3: Interoperability of MAS to support escalating NEO • Technical lead: Katia Sycara (CMU) • Participants: • CAMPS (Burstein, BBN) • OAA (Cheyer/Martin, SRI) • RETSINA (Sycara/Shehory, CMU)

18. TIE 3: Overview • Focus: flexibility, support of human collaboration during crisis • Means: Interoperation and substitutability among distributed heterogeneous agents • Human actors: • US ambassador to Kuwait • JFC • AMC

19. TIE 3: Scenario • Ambassador, JFC discuss evacuation via Retsina UI Messenger (UIM) and OAA MMM • JFC’s UIM monitors content, requests relevant info • WebMate provides text news, Maestro - video • Retsina flights agent begins providing schedules • OAA flights agent takes over when Retsina’s fails • UIM receives flights’ schedule and displays • OAA weather agent begins providing weather • Retsina weather agent takes over when OAA’s fails • Retsina route planner plans evacuation to KWI

20. TIE 3: Scenario (continued) • OAA phone agent provides info about roadblocks • Route planner replans to KWI airport • AMC rep uses a UIM to request CAMPS to provide airlift plan from KWI • Retsina Visual Sensor Agent (VSA) reports an explosion near KWI, displays it on MMM • JFC designate alternative abandoned airfield • Route planner replans to new destination • AMC via UIM requests a new plan from CAMPS • CAMPS returns a plan, presented by UIM

21. Scientific Claims Technical ChallengesTIE 3 1. Coherent communication of meaning between two heterogeneous MAS 2. Functional substitutability of agents 3. “Agentification” of legacy systems 4. Adaptivity at different levels: • Interfaces • MAS organization • Single Agent 5. Collaboration • human-human • human-agent • MAS

22. Scientific Claims - TIE 3 1. Coherent Communication of Meaning Short-Term Goals • Structure of interop agent • Protocols of interop agent Long-Term Goals • Interoperation services for the GRID? • ACL’s for the GRID 2. Functional Agent Substitutability Short-Term Goals • Provide languages and protocols for capability advertisement Long-Term Goals • Mechanisms for resolving mismatches of substitutable agent tasking and results

23. Scientific Claims - TIE 3 (continued) 3. “Agentification” of legacy systems Short-Term Goals • Providing wrapping mechanisms Long-Term Goals • Explore different mechanisms for agentification 4. Adaptivity/Robustness Short-Term Goals • Adaptive interface to user resources • Organizational adaptivity through middle agents Long-Term Goals • Explore additional mechanisms for organizational adaptivity

24. Scientific Claims - TIE 3 (continued) 5. Collaboration • Human-Agent Short-Term Goals • Giving tasks to agents Long-Term Goals • Principles for functional role allocation between human and agent • MAS Short-Term Goals • dynamic team formation Long-Term Goals • tradeoffs of different organizational structures (eg. Teams, hierarchies, heterarchies)

25. Closing remarks • NEO TIEs are based on existing systems but • Add new functionality • Require standardization (so far only partly achieved) • Require interoperation (OAA, TEAMCORE, RETSINA lead this and already provide) • Need communication infrastructure/protocol/language (RETSINA communicator, KQML, LARKS) • The need for working, fielded MAS stimulates research, solutions (interop, visualization, etc)