MultiAgent Systems Lecture 7 University Politehnica of Bucarest 2004 2005 Adina Magda Florea adinacs

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Multi-Agent SystemsLecture 7 University
Politehnica of Bucarest2004 - 2005Adina
Magda Floreaadina_at_cs.pub.rohttp//turing.cs.pub
.ro/blia_2005
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Distributed problem solving and planningLecture
outline

• 1 Distributed problem solving
• 2 Distributed planning
• 2.1 Centralized planning for distributed plans
• 2.2 Distributed planning for centralized plans
• 2.3 Distributed planning for distributed plans
• 2.4 Distributed planning and execution
• 3 An example Partial global planning

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1. Distributed problem solving

• Group coherence - agents want to work together -
  cooperative agents
• Competence - agents must find ways to work
  together - coordinate to cooperate
• Task and result sharing - an agent has many tasks
  to do and asks other agents to do some of its
  tasks then it should integrate the results
• Distributed planning - the problem to be solved
  is to design and execute a plan in a distributed
  manner, by many agents

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2 Distributed planning

• What can be distributed
• The process of coming out with a plan is
  distributed among agents
• Execution is distributed among agents
• Planning
• State representation and plan representation
• Search vs planning
• representation of changes to the world state
• representation of and reasoning about the plan
  (steps/actions)
• Linear planning
• Partial order planning
• Hierarchical planning
• Conditional planning

Planning ? Search
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• 2.1 Centralized planning for distributed plans
• Operators
• move(b,x,y) ? movetotable(b,x)
• Precond on(b,x) ? clear(b) ? clear(y)
  Precond on(b,x) ? clear(b)
• Postcond on(b,y) ? clear(x) ? Postcond
  on(b,T) ? clear(x) ? ?on(b,x)
• ?on(b,x) ? ?clear(y)

I'm Bill Agent1
I'm Tom Agent2
on(A,B) on(C,D) on(E,F) on(B,T) on(D,T)
on(F,T)
on(B,A) on(F,D) on(A,E) on(D,C) on(E,T)
on(C,T)
1. Given a goal description, a set of
operators, and an initial state
description generate a partial order plan
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• S1 move(B,T,A) To satisfy the preconditions,
  we have
• S2 move(A,B,E) S2 lt S1, S3 lt S4
• S3movetotable(E,F) S6 lt S4, S6 lt S5
• S4 move(F,T,D) Also
• S5 move(D,T,C) S2 threat to S3 ? S3 lt S2
• S6 movetotable(C,D) S4 threat to S5 ? S5 lt S4
• Then the partial ordering is S3 lt S2
  lt S1
• S6 lt S5 lt S4
• S3 lt S4
• S3 movetotable(E,F) S2 move(A,B,E) S1
  move(B,T,A)
• S6 movetotable(C,D) S5 move(D,T,C) S4
  move(F,T,D)
• Any total ordering that satisfies this partial
  ordering is a good plan for Agent1
• What if we have 2 agents?
• DECOMP1
• Subplan1 S3 lt S2 lt S1
• Subplan2 S6 lt S5 lt S4
• and S3 lt S4
• Agent1 S3 lt send(clear(F)) lt S2 lt S1
• Agent2 S6 lt S5 lt wait(clear(F)) lt S4

lt
lt
2. Decompose the plan into subproblems so as to
minimize order relations across plans 3. Insert
synchronization 4. Allocate subplans to agents
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• S3 movetotable(E,F) S2 move(A,B,E) S1
  move(B,T,A)
• S6 movetotable(C,D) S5 move(D,T,C) S4
  move(F,T,D)
• DECOMP2
• Subplan1 S3 lt S5 lt S4
• Subplan2 S6 lt S2 lt S1
• and S3 lt S2 and S6 lt S5
• Agent1 S3 lt send(don't_care(E)) lt wait(clear(D))
  lt S5 lt S4
• Agent2 S6 lt wait(don't_care(E)) lt wait(clear(D))
  lt S2 lt S1
• Obviously, DECOMP2 has more order relations among
  subplans than DECOMP1
• Therefore, we choose DECOMP1
• S3 lt send(clear(F)) lt S2 lt S1
• S6 lt S5 lt wait(clear(F)) lt S4
• But
• then back to DECOMP2

lt
lt
4. If failure to allocate subplans then redo
decomposition (2) If failure to allocate
subplans with any decomposition then redo
generate plan (1) 5. Execute and monitor subplans
I know how to move only D, E, F
I know how to move only A, B, C
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• 2.2 Distributed planning for centralized plans
• Each of the planning agents generate a partial
  plan in parallel then merge these plans into a
  global plan
• parallel to result sharing
• may involve negotiation
• Agent 1 - is specialized in doing
  movetotable(b,x)
• Agent 2 - is specialized in doing move(b,x,y)
• Agent 1 - based on Sf it comes out with the
  partial plan
• PAgent1 S3 movetotable(E,F) satisfies
  on(E,T)
• S6 movetotable(C,D) satisfies on(C,T)
• no ordering
• Agent 2 - based on Sf it comes out with the
  partial plan
• PAgent 2 S1 move(B,T,A), S2
  move(A,B,E) satisfies on(B,A) ? on(A,E)
• S4 move(F,T,D), S5 move(D,T,C) satisfies
  on(F,D) ? on(D,C)
• ordering S2 lt S1 and S5 lt S4
• Merge PAgent1 with PAgent2 by checking
  preconditons and threats
• Establish thus order S3 lt S2, S6 lt S5, S3 lt S4
  order of PAgent2
• Then give any instance of this partial plan to an
  execution agent to carry it out

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• The problem is decomposed and distributed among
  various planning specialists, each of which
  proceeds then to generate its portion of the plan
• similar to task sharing
• may involve backtracking
• Agent 1 - knows only how to deal with 2-block
  stacks
• Agent 2 - knows only how to deal with 3-block
  stacks

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• 2.3 Distributed planning for distributed plans
• a) Plan merging
• Agents formulate local plans to satisfy their
  goals
• Local plans are exchanged
• Local plans are combined analyzing for positive
  and negative interaction
• Add messages and/or timing commitments to resolve
  negative plan interactions and to exploit
  positive plan interactions
• Interacting situations
• Positive interactions between plans
• redundant actions
• static detection sequencing
• favour actions
• dynamic detection incorporation
• Negative interactions between plans
• harmful actions
• exclusive actions
• incompatible actions

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• movehigh(b,x,y)
• Precond have_lifter ? clear(b) ? clear(y) ?
  on(y,z) ? z? T
• Postcond on(b,y) ? clear(x) ? ?on(b,x) ? ?
  clear(y) ? free_lifter
• pick_lifter
• Precond free_lifter
• Postcond have_lifter ? ?free_lifter
• Agent1 S1move(B,T,A) lt S2 pick_lifter lt S3
  movehigh(E,T,B)
• Agent2 R1move(C,T,D) lt R2 pick_lifter lt R3
  movehigh(F,T,C)

Negative interactions what type?
R1
S1
need_l
S2
S3
Sf1
free_l
R2
R3
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Positive interactions

• Give examples of positive interactions
• redundant
• favor
• Problems with the approach?

• b) Iterative plan formation
• build all feasible plans
• build partial order plans to facilitate plan
  merging
• build abstract plans to be iteratively refined
• - see next section and PGP section

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• c) Hierarchical distributed planning
• Design plans on several levels of abstraction
• Use abstract plans
• Abstract operator - a kind of macro-operator
  sequence of applicable operators

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Hierarchical behavior-space search
algorithm 1. Level ? 0, Agent_List Agent1, ,
AgentN 2. for every Agenti in Agent_List
do 2.1 Agenti sends description of Gi and Pi
to every Agentj, j1,N, j?i 2.2 Agenti gets
Gj, Pj from Agentj, j1,N, j?i 2.3 if Pi is
compatible with Pj, j1,N, j?i then
Agenti removes itself from Agent_List 3. if
Agent_list then exit 4. Be N the new number
of agents in Agent_List 5. Sort agents in
Agent_List 6. for i1,N-1, cf. ordering
do 6.1 make Agenti the current superior 6.2
Agenti determines conflicts between Pi 6.3
if conflicts to be resolved at a lower level
then (a) Level ? Level 1 (b)
Agent_List Agenti1, , AgentN (c) go
to step 2 6.4 send Pi to each Agentj, ji1,
N 6.5 for ji1, N do - Agentj checks
compatibility of Pj with Pi and replan, if nec.

• A kind of CSP
• Ordering
• - what heuristic?

Add exit condition for no solution
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• 2.4 Distributed planning and execution
• Real world incomplete and incorrect information
• a) Contingency planning
• Conditional planning - deals with incomplete
  information by constructing a conditional plan
  that accounts for each possible situation or
  contingency that could arrive
• sensing actions
• a context of a plan step, i.e., a union of
  conditions on the environment that must hold in
  order for a step to be executed ? introduces
  disjunctive steps conditional links among plan
  steps

Start
on(A,B)?clear(C)?clear(A)
Checkarm(Ag1)
Ask Ag2 to move(A,B,C)
?armbroken(Ag1)
armbroken(Ag1)
move(A,B,C)
Context ?armbroken(Ag1)
Negotiate with Ag2 for it to achieve move
Plan to achieve on(B,A)
Finish
on(B,A)?on(A,C)
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• b) Execution monitoring
• The agent does not execute the plan with "its
  eyes closed" - It monitors what is happening
  while it executes the plan and it can do
  replanning to achieve a goal in a new situation
• Conditional planning thinks before to several
  alternatives
• Monitoring and replanning defers the job I
  shall see what to do if new conditions occur
• c) Social laws
• What actions are legal to be executed in a
  certain context
• Find conflicting situations, analyze what
  concurrent actions lead to these situations and
  prohibit such concurrent actions by social laws
• It is fit, in general, for loosely coupled
  subproblems / subplans

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3 Partial Global Planning

• Initially applied in the Distributed Monitoring
  Vehicle (DVM) Testbed, then extended to be domain
  independent
• Integrates planning and execution
• Coordination by means of partial plans exchange
• Partial plans abstract plans partial ordering
  ? plan merging
• The domain - unpredictable, unreliable
  information
• The tasks are inherently distributed each agent
  performs its own task
• The agents are not aware of the global state of
  the system however there is a common goal
  converge on a consistent map of vehicle movements
  by integrating the partial tracks formed by
  different agents into a single complete map or
  into a consistent set of local maps distributed
  among agents
• Cooperative agents (collectively motivated)

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• 3.1 Aircraft monitoring scenario
• each type of aircraft produces a characteristic
  spectrum of acoustic frequencies
• signals may be improperly sensed, there is
  ghosting and environmental noise
• there are two agents A and B whose regions of
  interest overlap each agent receives data only
  about its own region, from its acoustic sensor
• the goal is to identify any aircraft that is
  moving through the region of interest, determine
  their types and track them through regions

Data input
Final solution
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• 3.2 Agent functioning
• 1. Represent its own expected activity by a set
  of local (tentative) plans, at two levels higher
  level (abstract plans) and detailed level local
  plans may involve alternative actions depending
  on the result of previous actions and changes in
  the environment
• ? conditional plans hierarchical plans
• 2. Communicate abstract local plans to the other
  agents and get from them such plans ? another
  form of communication
• 3. Model collective activity of the agents by
  forming Partial Global Plans and finding out how
  they can be improved for better coordination
• identify when the goals of one or more agents can
  be considered subgoals of a single global goal ?
  partial global goal
• construct a PGP and identify opportunities for
  improved coordination
• search for an improved PGP
• 4. Based on 3, propose changes to one or more
  agents' plans
• ? negotiation
• 5. Modify its local plan according to the
  proposal and plan what and when results will be
  communicated to the other agents

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• 2 types of problem-solving activities
• task-level activities - build a map of vehicle
  movements
• meta-level activities - decide how and with whom
  to coordinate
• Result sharing - agents exchange appropriate
  results at the right time
• Task sharing - allow agents to propose potential
  plans that involve the transfer of tasks among
  them

A Process 1/3 data
B Process 1/3 data
Who? Process 1/3 data
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• 3.3 Plan representation
• A plan represents future activity at two levels
  of detail
• at the higher level it outlines the major steps
  it expects to take to achieve its goal - abstract
  plan
• at a detailed level it specifies primitive
  actions to achieve the next step in the abstract
  plan as the plan is executed, new details are
  added incrementally
• action
• Prec preconditions for the action
• Post results of the action
• D - the set of data to be processed by the
  action
• P - the set of procedures to be applied to the
  data
• Tstart - the estimated start time of the action
• Tend - the estimated end time of the action
• abres - an estimate of the characteristics of
  and confidence in the abstract partial result
  that will be developed as conclusion of action

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• 3.4 PGP formation and coordination
• (1) Task decomposition
• (2) Local plan formation
• (3) Local plan abstraction
• (4) Communication about local abstract plans
• Meta-Level Organization specifies roles and
  controls communication
• For each agent, the MLO specifies
• - the agents it has authority over
• - the agents that have authority over it
• - the agents that have equal authority
• (5) Partial global goal identification
• Set of operators that generate global goals based
  on local goals

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• (6) Partial global plan construction and
  modification
• partial global goal
• plan-activity-map plan actions to be executed
  concurrently by itself and the other agents,
  including costs and expected results of actions -
  PGP
• Criteria for rating the actions
• the action extends a partial result (vehicle
  tracking hypothesis)
• the action produces a partial result that might
  help some other agents in forming partial results
• how long the action is expected to take
• (7) Communication planning
• From the modified plan-activity-map, the agent
  builds a solution-construction-graph how the
  agents should interact, including specifications
  about what partial results to exchange and when
  to exchange them
• (8) Translate to local level the activities in
  the revised plan
• (9) If authority, send PGP to the other agents

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• References
• E.H. Durfee. Distributed problem solving and
  planning. In Multiagent Systems - A Modern
  Approach to Distributed Artficial Intelligence,
  G. Weiss (Ed.), The MIT Press, 2001, p.121-164.
• V.R. Lesser. A retrospective view of FA/C
  distributed problem solving. IEEE Trans. On
  Systems, Man, and Cybernetics, 21(6), Nov/Dec
  1991, p.1347-1362.
• E.D. Durfee, V.R. Lesser Partial global planning
  A coordination framework for distributed
  hypothesis formation. IEEE Trans. On Systems,
  Man, and Cybernetics, 21(5), Sept. 1991,
  p.1167-1183.
• K.S. Decker, V.R. Lesser. Generalizing the
  partial global planning algorithm. International
  Journal of Intelligent Cooperative Information
  Systems, 1(2), 1992, p. 319-346.
• S. Russell, P. Norvig. Artificial Intelligence A
  Modern Approach. Prentice hall, 1995, Ch. 11, 12,
  13.

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