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IMPACT Multi-Agent Planning Research

IMPACT Multi-Agent Planning Research. Dana Nau University of Maryland. 1. Motivation: Noncombatant Evacuation Operations (NEOs). Goal: assist DOS to evacuate people whose lives are in danger noncombatants nonessential military personnel host-nation citizens third country nationals.

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IMPACT Multi-Agent Planning Research

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  1. IMPACT Multi-Agent Planning Research Dana Nau University of Maryland 1

  2. Motivation: Noncombatant Evacuation Operations (NEOs) • Goal: assist DOS to evacuate people whose lives are in danger • noncombatants • nonessential military personnel • host-nation citizens • third country nationals • Characteristics: • Joint task force • geographically distributed • often multinational • Uncertainty; complexity (200+ tasks) • US Ambassador is senior authority

  3. Difficulties with NEOs • Multi-agent planning • Planning is the responsibility of the geographic commanders • Resources: Doctrine, Exercises, DOS, EAP, etc. • Supplies are not centralized • Information about supplies is not centralized either • Potential for conflicts among simultaneous operations • Example (not a NEO): moving Apache helicopters from Ramstein to Albania during the Kosovo crisis

  4. Key Technologies • Access to distributed,heterogeneous data sources • Seamless interoperability between different software capabilities • Ability to coordinate multiple agents • Scalable, high performance planning systems • create plans • interact with the above data and software sources Already being developed in the IMPACT project To be added to IMPACT

  5. What Planning Is • Generate sequences of actions to perform tasks and achieve objectives • Driving force • The need for ways to aid human planning • Example application areas • military operations and logistics • design and manufacturing • space exploration

  6. Classical planning theory: Either purely symbolic (AI planning) or purely numeric (OR planning) Single agent (the planner) Perfect information No interaction with users What’s needed in practice: Mixed symbolic & numeric computation Multiple agents Imperfect information, external information sources Human user in control of planning Limitations of Classical Planning The above limitations may be overcome using a mix of IMPACT and scalable planning algorithms

  7. Approach • Extend IMPACT to include (1) planning actions (2) temporal constraints for plans (3) complex planning tasks

  8. (1) Planning Actions • Extend IMPACT rules so that heads of rules contain planning actions • Example: • if an item’s current inventory level has dropped below its restocking threshold, then create a plan to restock it at its stock level, by a given date, and within the given budget DO build-plan(restock(‘widget-25’,X.rstocklev,8/30/99,100k)) <= in(X,oracle:select(‘inventory”,item,=,‘widget-25’)) & <(X.qty,X.threshold). • Problem: • semantics of agent programs forces all executed actions to have a well-defined add/del list • build-plan(…) may or may not be achievable, so we don’t know what the add/del list should be

  9. Planning Actions (Contd.) • Extend IMPACT rule syntax to allow heads of rules to: • contain a planning action and • support actions whose outcomes may involve “conditional” add/delete lists of the form “fail” or “add/delete <specified lists>”. • Extend the IMPACT implementation to support invocation of such planning actions

  10. A tcass (temporally-constrained action status set) is a set of action status atoms with temporal constraints on the actions Example: < {a1,a2,a3}, { st(a1) = st(a2), et(a1) < et(a2), st(a3) - st(a2) < 5, et(a3) - et(a2) < 3} > where st(a) denotes a’s start time et(a) denotes a’s end time Increase the expressive power of agent rules by allowing rules of the form: Op tcass <= Body Also allow replacing tcass by a call to a planner which generates a tcass as its output We plan to refine the syntax and study semantics of such programs sound/complete status-set computation algorithms implementation techniques and experiments applications such as NEO planning (2) Temporal Constraints for Plans

  11. (3) Complex Tasks • Extend agent program rules so that the head of an agent program rule can have the form Op htn <= Body where htn is either • an HTN (a hierarchical task network) • (see next slide) • a call to an HTN generation program • e.g., the SHOP planning system (described later) • We plan to refine the syntax and study the • semantics • computation algorithms for feasible status sets • implementation and experiments • applications

  12. HTN Planning: An Example • Select Helicopter Launching Base • Select possible area (A) • Transport sec. force (F,A,H) • Embark sec. force (F,H) • Fly(H,A) • Disembark (F,H,A) • Position security force (F,A) • Transport fuel to (A) • ... Select Helicopter Launching Base alternative methods Launch from Carrier Battle Group Establish Base within Flying Distance Transport helicopters available (H) Transport helicopters available (H) • Decompose tasks into (more tactical) subtasks • Consider restrictions (e.g., transport helicopters available) • Resolve interactions (e.g., deploy security force first) • If necessary, backtrack and try other methods Security force available (F) Helicopters have air refuel. capability (H)

  13. Leverage (1) • SHOP - Simple Hierarchical Ordered Planner • New HTN planning system [Nau et al., IJCAI-99] • Outgrowth of some of the ideas explored in the Bridge Baron • Sound and complete over a large class of planning problems • Much more expressivity than most other planning systems • Mixed symbolic & numeric computations • External information sources • Solves standard benchmark problems orders of magnitude faster than other domain-independent planning systems • Complete implementation in Common Lisp • Available via FTP; downloaded by dozens of researchers • Implementation in Java underway

  14. Leverage (2) • HICAP: Non-Combatant Evacuation Planning • Joint ongoing work with the Naval Research Laboratory • Combines SHOP with case-based reasoning • Makes use of military doctrine and previous successful plans • Nominated for best-paper award at ICCBR-99 • Héctor Muñoz will demo HICAP during the demo session

  15. Multi-Agent Planning • We intend to do the following • Incorporate the extensions mentioned earlier • planning actions • temporal constraints for plans • complex planning tasks • Develop protocols by which multiple agents may coordinate planning activities with one another • Derive results showing that (under certain conditions to be determined), such protocols guarantee convergence on a plan • This will ensure termination within predictable running times • Develop applications • TBD, but a likely possibility is multi-agent planning for NEOs

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