Dana Nau

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IMPACT Multi-Agent Planning Research

• Dana Nau
• University of Maryland

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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

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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

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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
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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

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Limitations of Classical Planning

• Classical planning theory
• Either purely symbolic (AI planning) or purely
  numeric (OR planning)
• Single agent (the planner)
• Perfect information
• No interaction with users

• Whats needed in practice
• Mixed symbolic numeric computation
• Multiple agents
• Imperfect information, external information
  sources
• Human user in control of planning

The above limitations may be overcome using a
mix of IMPACT and scalable planning algorithms
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Approach

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

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(1) Planning Actions

• Extend IMPACT rules so that heads of rules
  contain planning actions
• Example
• if an items 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))
• lt in(X,oracleselect(inventory,item,,widget-
  25))
• lt(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
  dont know what the add/del list should be

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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 ltspecified listsgt.
• Extend the IMPACT implementation to support
  invocation of such planning actions

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(2) Temporal Constraints for Plans

• A tcass (temporally-constrained action status
  set) is a set of action status atoms with
  temporal constraints on the actions
• Example
• lt a1,a2,a3,
• st(a1) st(a2),
  et(a1) lt et(a2), st(a3) -
  st(a2) lt 5, et(a3) - et(a2) lt
  3 gt
• where
• st(a) denotes as start time
• et(a) denotes as end time

• Increase the expressive power of agent rules by
  allowing rules of the form
• Op tcass lt 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

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(3) Complex Tasks

• Extend agent program rules so that the head of an
  agent program rule can have the form
• Op htn lt 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

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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)
Security force available (F)
Helicopters have air refuel. capability (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

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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

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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

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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