Tohfa tul-raza-fi-milad-e-mustafa-by-dr-raza-muhammad-shah-hashmi-pdf
Dr. Muhammad Adnan Hashmi
description
Transcript of Dr. Muhammad Adnan Hashmi
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An Agent Oriented Programming Language integrating Temporal
Planning and the Plan Coordination Mechanisms
Dr. Muhammad Adnan Hashmi
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Outline Introduction
Background Problem Overview
Plan Coordination Mechanisms Coordinated Planning Problem Proactive-Reactive Coordination Problem
P-CLAIM: AOP Language supporting Temporal Planning Language Definition Planning Mechanism
Conclusion
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Outline Introduction
Background Problem Overview
Plan Coordination Mechanisms Coordinated Planning Problem Proactive-Reactive Coordination Problem
P-CLAIM: AOP Language supporting Temporal Planning Language Definition Planning Mechanism
Conclusion
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What is an Agent? An agent is a computer system capable of autonomous
action in some environment in order to meet its design objectives (goals).
Achievement of goals is important for agents
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Planning Finding a sequence of actions that takes agent from
Initial State to Goal State
Initial state:clear(c),on(c,a),ontable(a),clear(b),ontable(b),handempty
ca b
Goal:on(a,b),on(b,c) c
ab
ca b ca b ca
bc
ab
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Multi-Agent Planning Multiple agents sharing the same environment
Independent planning is not sufficient Coordination of plans is vital
Coordination of plans Removing conflicts (negative interactions) Utilizing help relations (positive interactions)
A
B
DA
DB
A
B
DA
DB
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How to Program Agents? Agent Oriented Programming (AOP) Languages allow to
program intelligent and autonomous agents
Main Characteristics Mental State: Beliefs, Goals, Commitments Reasoning Mechanism Capabilities, Services Communication
Some Languages Agent-0 [Shoham 1993], 2APL [Dastani 2008], AgentSpeak (L)
[Rao 1996]
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Outline Introduction
Background Problem Overview
Plan Coordination Mechanisms Coordinated Planning Problem Proactive-Reactive Coordination Problem
P-CLAIM: AOP Language supporting Temporal Planning Language Definition Planning Mechanism
Conclusion
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Problem Statement Current AOP languages
Follow a reactive (PRS based) approach Do not support temporal planning
Only a few support planning
Problems Execution without planning may result in the goal failures
Agent can reach a dead end Conflicts can arise among different agents Actions’ duration is important
Real world actions take place over a timespan
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Objectives Propose a programming language that endows agents with
planning skills Has temporal planning Deals with uncertainty of the environment Incorporate reactivity by dealing with on the fly goals having
different priorities
Propose coordination mechanisms for the plans having different priorities
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Outline Introduction
Background Problem Overview
Plan Coordination Mechanisms Coordinated Planning Problem Proactive-Reactive Coordination Problem
P-CLAIM: AOP Language supporting Temporal Planning Language Definition Planning Mechanism
Conclusion
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Assumptions Two agents α and β sharing the same environment
Agent α having higher priority (reactive) goals Agent β having normal priority (proactive) goals
Actions have: Preconditions Add effects Delete effects
Two possible conflicts among plans Causal link threat Parallel actions interference
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Two Possible Conflicts Causal Link (A1, A2, p)
Action A1 adds an effect p Action A2 needs this effect No action between A1 and A2 adding p
Causal Link Threat If an action A deletes p and lies between A1 and A2, then A
threatens the causal link (A1, A2, p)
A1 A2p
A ¬p
Threat
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Two Possible Conflicts Causal Link (A1, A2, p)
Action A1 adds an effect p Action A2 needs this effect No action between A1 and A2 adding p
Parallel Actions Interference Actions A1 and A2 lie in parallel Either one of them deletes the preconditions or add effects of the
other
A1
A2
¬p
p
A1
A2
p
¬p
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Outline Introduction
Background Problem Overview
Plan Coordination Mechanisms Coordinated Planning Problem Proactive-Reactive Coordination Problem
P-CLAIM: AOP Language supporting Temporal Planning Language Definition Planning Mechanism
Conclusion
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Coordinated Planning Problem Prerequisite:
Plan Pα of Agent α
Our Aim: Compute a Plan Pβ for Agent β
Has no conflict with Pα
Avails the cooperative opportunities offered by Pα
Solution: Non Temporal Domains µ-SATPLAN Temporal Domains Coordinated-Sapa
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SATPLAN [Kautz and Selman
2006]A Classical Planner that Finds Optimal
Plans in Non-Temporal Domains
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SATPLAN
Compiler(encoding)
satisfyingmodel
Plan
Increment plan lengthIf unsatisfiable
Planning Problem • Init State• Goal• Actions
CNF Simplifier(polynomial inference)
Solver(SAT engine/s)
Decoder
CNF
Propositional formula in conjunctive normal form (CNF)
We Use GraphPlan Encoding
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Constructing the planning graph Level P1: All literals from the initial state Add an action in level Ai if all its preconditions are present in
level Pi Add a proposition in level Pi if it is the effect of some action in
level Ai-1 Maintain a set of exclusion relations to eliminate incompatible
propositions and actions
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GraphPlan Encoding
Fact Act1 Act2
Act1 Pre1 Pre2
¬Act1 ¬Act2
Act1
Act2
Fact
Pre1
Pre2
Can create such constraints for every node in the planning graph
Only involves facts and actions in the graph
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µ-SATPLAN
An Extension of SATPLAN that Computes Coordinated Plans in Non-
Temporal Domains
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Handling Causal Link Threat
While constructing the planning graph for Agent β, don’t add an action O at level Ai if It has an effect ¬p, and There is a causal link (Aj, Ak, p) in plan Pα, and j ≤ i ≤ k
Action O threatens Causal Link (Aj, Ak, p)
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Handling Positive Interactions & Parallel Actions Interference For each time step i in the plan of Agent α, create an
action NoName(i)
Add(NoName(i)) All the effects added by Pα at time step i
Del(NoName(i)) All the effects deleted by Pα at time step i
Pre(NoName(i)) All the preconditions of actions of Pα at time step i
Explicitly add all the NoName actions in the planning graph of Agent β
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Handling Positive Interactions Pα = {α1(0), α2(0), α3(1)} Eff(α1) = a0, Eff(α2) = a1, Eff(α3) = a2
Eff( NoName(0) ) = {a0,a1}, Eff( NoName(1) ) = {a2}
a0
a1
a2
a4
a5
a6a7
a8
Level 0 Level 1 Level 2
a4
a6
NoName(0)
β1
a6
a4
a0
a1
NoName(1) a2
a0
a1
β2
a5β3
a6
a5
a4
a7
β4
Planning Graph of Agent β
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Handling Positive Interactions Partial CNF Sentence
a8 β4 β4 a0 a7 a0 β2 NoName(0) a7 β3 β2 a5 …
Add NoName actions as unary clauses to CNF NoName(0) NoName(1)
Problem Solution
a0
a1
a2
a4
a5
a6a7
a8
Level 0 Level 1 Level 2
a4
a6
NoName(0)
β1
a6
a4
a0
a1
NoName(1) a2
a0
a1
β2
a5β3
a6
a5
a4
a7
β4
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Example Plan Generated (Logistics)
Time Action Performed
0 DRIVE-TRUCK (TRUCK1, LOC1-1, LOC1-2, CITY1)
1 LOAD-TRUCK (PACKAGE1, TRUCK1, LOC1-2)
1 DRIVE-TRUCK (TRUCK2, LOC2-2, LOC2-1, CITY2)
1 LOAD-PLANE (PACKAGE2, PLANE1, LOC1-1)
2 DRIVE-TRUCK (TRUCK1, LOC1-2, LOC1-1, CITY1)
2 FLY-PLANE (PLANE1, LOC1-1, LOC2-1)
3 UNLOAD-PLANE (PACKAGE2, PLANE1, LOC2-1)
3 UNLOAD-TRUCK (PACKAGE1, TRUCK1, LOC1-1)
4 LOAD-PLANE (PACKAGE1, PLANE2, LOC1-1)
4 LOAD-TRUCK (PACKAGE2, TRUCK2, LOC2-1)
5 FLY-PLANE (PLANE2, LOC1-1, LOC2-1)
5 DRIVE-TRUCK (TRUCK2, LOC2-1, LOC2-2, CITY2)
6 UNLOAD-PLANE (PACKAGE1, PLANE2, LOC2-1)
6 UNLOAD-TRUCK (PACKAGE2, TRUCK2, LOC2-2)
Time Action Performed
0 LOAD-TRUCK (PACKAGE4, TRUCK2, LOC2-2)
1
2
3 LOAD-PLANE (PACKAGE3, PLANE1, LOC2-1)
4 FLY-PLANE (PLANE1, LOC2-1, LOC1-1)
4 UNLOAD-TRUCK (PACKAGE4, TRUCK2, LOC2-1)
5 UNLOAD-PLANE (PACKAGE3, PLANE1, LOC1-1)
6 LOAD-PLANE (PACKAGE4, PLANE2, LOC2-1)
6 LOAD-TRUCK (PACKAGE3, TRUCK1, LOC1-1)
7 FLY-PLANE (PLANE2, LOC2-1, LOC1-1)
7 DRIVE-TRUCK (TRUCK1, LOC1-1, LOC1-2, CITY1)
8 UNLOAD-PLANE (PACKAGE4, PLANE2, LOC1-1)
8 UNLOAD-TRUCK (PACKAGE3, TRUCK1, LOC1-2)
Pα Pβ
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Outline Introduction
Background Problem Overview
Plan Coordination Mechanisms Coordinated Planning Problem Proactive-Reactive Coordination Problem
P-CLAIM: AOP Language supporting Temporal Planning Language Definition Planning Mechanism
Conclusion
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Proactive-Reactive Coordination Prerequisite:
Reactive plan Pα of Agent α Proactive plan Pβ of Agent β
Our Aim: Modify plan Pβ such that:
It has no conflict with Pα
Avails the cooperative opportunities offered by Pα
Solution: Plan Merging Algorithm
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Case Study Tasks of Rescue Agent
Rescue the Victims Tasks of Analyzer Agent
Analyze the goal cells Call the central agent
Constraints One agent in a cell Hyper energy cells
Needs fuel or energy to enter Agent should have key to open door
Rescue Agent : αAnalyzer
Agent : β
D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CBA
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Conflict Resolution Threat-Repair Link (A1, A2, p)
Action A1 deletes p A2 is a subsequent action and adds p
A1 is called Threat Action A2 is called Repair Action
B1 B2p
A1 -p A2 p
Threat
Repair
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Valid and Possibly Valid Time Stamps Possibly Valid Time Slot for an action A
All preconditions are met No parallel actions interference
P[1] a bh P[2] b c
-d P[3] c e P[4] e f P[5] f g
P[1] b d-h
P[6] g i P[7] i h g
Valid Time Slot for an action A All preconditions are met No parallel actions interference Either:
No causal link threat Repair Action exist before the deadline
P[1] a bh P[2] b c
-d P[3] c e P[4] e f P[5] f g
P[1] b d-h
P[6] g i P[7] i h g
P[2] d k P[3] k h
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Plan Merging Algorithm
Fix all the actions of Reactive Plan Pα on timeline
For every action CA of Proactive Plan
Search for the first Possibly Valid Time Slot T on timeline
Reason about the time slot T There could be 5 cases at T
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Plan Merging AlgorithmCase 1: No causal link threat by CA at T
Assign Time Slot T to CA
EXAMPLE Current Action: Move(A1, A2)
Returned Time Slot: 0 - 5 Any Threat? : No Assign Time Slot 0 – 5 to CA
D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CBA
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Plan Merging AlgorithmCase 2: CA threatens a Causal Link but Repair Action exist
Assign Time Slot T to CA Save a Possible Threat <ThreatAction, RepairAction, Deadline>
EXAMPLE Current Action: Move(A4, A5)
Time Slot: 20 - 25 Any Threat? : Yes (Agent α needs A5 at time 40-45) Repair Action: Move(A5, A6) Assign Time Slot 20 - 25 to Move (A4, A5) Save <Move(A4, A5), Move(A5, A6), 40>
D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CBA
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Plan Merging AlgorithmCase 3: It is a Repair Action but can not meet a deadline of
some Threat Action Backtrack to the Threat Action,find another time stamp
EXAMPLE Current Action: Move (A8, A9)
Returned Time Slot: 50 - 55 Any Threat?: Yes (Agent α needs A9 at 85-110) Repair Action : Move (A9, B9) Save <Move(A8, A9), Move(A9, B9), 85>
Next Action: AnalyzeCell (A9) Time Slot Assigned: 55 - 70
D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CBA
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Plan Merging Algorithm Next Action: CallCentral (A9)
Time Slot Assigned: 80 – 90 Next Action: Move (A9, B9)
Is it a Repair Action? : Yes Meet all deadlines?: No (Agent α needs A9 at 85) Backtrack to action Move(A8, A9)
Find another Time Slot New Time Slot: 110 – 115 (Valid Time Slot)
D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CBA
Attention
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Plan Merging AlgorithmCase 4: All the effects of CA are already achieved WHAT TO DO? Mark CA as redundant POST PROCESSING Remove all redundant actions from the plan Recursively remove all actions which achieve only the
preconditions of removed action
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Plan Merging AlgorithmEXAMPLE Current Action: OpenDoor (C11)
Returned Time Slot: 172 - 175 Redundant(OpenDoor(C11)) true
Because openedDoor(C11) is true at time 172
When the plan is returned Remove OpenDoor(C11) from plan Also remove TakeKey(C11, key1) from plan
D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CBA
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Plan Merging AlgorithmCase 5: Action CA’s preconditions can not be achieved
Remove action CA from the plan and compute a plan to achieve effects of CA
I = State just before CA G = Effects (CA)
Plan should have no conflict with Reactive Plan Pβ and if CA is a repair action, repair effects must meet their deadline
ReplacementPlan = Coordinated-Sapa (I, G, Pβ)
If a plan is returned, replace the removed actions with the plan
If a deadline is violated, backtrack to the threat action
If no plan possible, then remove another action CA + 1
G = G U Effects (CA + 1) \ Pre (CA + 1)
Use Coordinated-Sapa
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Plan Merging AlgorithmEXAMPLE Current Action: TakeEnergy(B13, energy1) Preconditions can not be achieved
Repair the plan
D
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CBA
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Plan Repair Algorithm Create a CPP by removing TakeEnergy(B13, energy1)
I = { at(β, B13), at(energy1, B13), at(energy2, B13) } G = { hasEnergy(β, energy), at(β, B13)}
Call Coordinated-Sapa to solve this CPP Coordinated-Sapa returns fail Why? energy2 is also needed by Agent α
D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CBA
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Plan Repair Algorithm Create another CPP by removing Move(B13, A12)
I = { at(β, B13), at(energy1, B13), at(energy2, C15) } G = { at(β, A12) }
Call Coordinated-Sapa to solve this CPP A plan is returned to enter A12 by taking the fuel from D14
POST PROCESSING This plan will become a replacement for both
TakeEnergy(B13, energy1) and Move(B13, A12)
D
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CBA
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Outline Introduction
Background Problem Overview
Plan Coordination Mechanisms Coordinated Planning Problem Proactive-Reactive Coordination Problem
P-CLAIM: AOP Language supporting Temporal Planning Language Definition Planning Mechanism
Conclusion
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An AOP language having: Cognitive aspects specific to intelligent agents Communication primitives Mobility primitives Temporal planning capability
P-CLAIM Agent: Is autonomous, intelligent and mobile Has a mental state containing knowledge, goals, and capabilities Is able to communicate with other agents Entails a planning based behaviour Achieves goals based on their priorities Maintains the stability of the plan in the dynamic environments
P-CLAIM
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Defining P-CLAIM Agent
defineAgent agentName{ parent = null | agentName ; knowledge = null ;| {knowledge1; …; knowledgem} goals = null ;| {goal1; … ; goaln} activities = null ; {activity1 … activityo} actions = {action1 … actionp} agents = null ; | {agName1, agName2, …, agNameq}
}
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Activities
defineAgent agentName{ parent = null | agentName ; knowledge = null ;| {knowledge1; …; knowledgem} goals = null ;| {goal1; … ; goaln} activities = null ; {activity1 … activityo} actions = {action1 … actionp} agents = null ; | {agName1, agName2, …, agNameq}
}activityity = name { message = null | message ; conditions = null | condition ; do { process } effects = null ;| { effect1 ; …; effectf }
}
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Actions
defineAgent agentName{ parent = null | agentName ; knowledge = null ;| {knowledge1; …; knowledgem} goals = null ;| {goal1; … ; goaln} activities = null ; {activity1 … activityo} actions = {action1 … actionp} agents = null ; | {agName1, agName2, …, agNameq}
}action = name { message = null | message ; conditions = null | condition ; do { process } duration = dur ;
}
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Outline Introduction
Background Problem Overview
Plan Coordination Mechanisms Coordinated Planning Problem Proactive-Reactive Coordination Problem
P-CLAIM: AOP Language supporting Temporal Planning Language Definition Planning Mechanism
Conclusion
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Agent Definition to Planning (Translator)
Translator(JavaCC)
Knowledge Goals Activities Actions
Initial State Goals Methods Operators
Problem File Domain File
Agent Description File
Planner
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Agent Life Cycle
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Messages Handler
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Waits for messages from other agents
Message is a request to achieve a goal? Assigns priority to the goal Reactive Goal puts it in Global Reactive Goals (GRG) queue Proactive Goal puts it in Global Proactive Goals (GPG) priority
queue
Message is an information? Store the information in the knowledge base of the agent
Messages Handler
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Planner
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Temporal Converter
Compute Plan
Main Algorithm
GRG GPG
1- Fetch goals one by one
from GRG and GPG and calls Compute_Plan to compute a plan for the
goal
2- GPG Goals are accessed
only when GRG is empty
3- Sends a suspension
signal to Executor if the
goal is reactive i.e. from GRG
Planner
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Plan Computation JSHOP2 algorithm [Nau et el. 2003]
is used to compute a totally ordered plan for each goal
An HTN planning algorithm
Decomposes the task into sub-tasks by applying methods
Recursively applies the same procedure on every composite sub-task until there are only primitive tasks
T1
M1
T11 T12
M12M11
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Temporal Converter
Input to procedure A totally ordered plan Actions’ information (Add, Del, Pre, Durations)
Output of procedure A position constrained parallel plan
Every action is assigned a time stamp Multiple actions can possibly lie in parallel
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P[1]a-a+d+e+f
P[2]f-f+g+h
P[3]e +i
P[4]h -h+j
P[5]gi +k
P[6]k +l P[7]l +m P[8]m +n+c P[9]
c mn
-c
P[10]e-k-c+h
P[1]a-a+d+e+f
P[2]f-f+g+h
P[3]e +i P[4]h -h+j P[5]g
i +k P[6]k +l P[7]l +m P[8]m +c P[9]c m -c P[10]e
-k-c+h
Input Plan
Output Plan
Makespan Gain: 30%
Temporal Converter (Example)
Makespan =150
Makespan =105
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Merging the New Plan to Global Plan
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Merging the New Plan to Global Plan
Planner
Append at the end of PexecMerge at the start of Pexec
Proactive GoalReactive Goal
Plan Under Execution (Pexec)
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Schedule Handler and Executor
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Schedule Handler and Executor
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Plan Mender
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Plan Mender
SP(Pexec[1]) SP(Pexec[2])SP(Pexec[3])
GSP(Pexec[4])
SW
Compute Plan Using Sapa
No Plan
Compute Plan Using Sapa
ReplacementPlan ContinuationPlan
Pexecnew = ReplacementPlan + ContinuationPlan
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Outline Introduction
Background Problem Overview
Plan Coordination Mechanisms Coordinated Planning Problem Proactive-Reactive Coordination Problem
P-CLAIM: AOP Language supporting Temporal Planning Language Definition Planning Mechanism
Conclusion
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Conclusion An agent oriented programming language supporting:
Temporal Planning Plan Repairing Dealing with different priority goals
Coordinated Planning Problem Computing plan while coordinating it with another plan
SATPLAN µ-SATPLAN Sapa Coordinated-Sapa
Proactive-Reactive Coordination Problem Modifying a plan to remove conflicts with a higher priority
plan Plan Merging Algorithm
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Publications1. A. El Fallah Seghrouchni, M. A. Hashmi, “Multi-Agent Planning”, Book
chapter in Software Agents, Agent Systems and Their Applications. M. Essaaidi et al. (Eds.), 2012, IOS Press.
2. Y. Dimopoulos, M. A. Hashmi, P. Moraitis, “µ-SATPLAN: Multi-Agent Planning as Satisfiability”, Knowledge Based Systems Journal, 2011
3. M. A. Hashmi, A. El Fallah Seghrouchni, “Merging of Temporal Plans supported by Plan Repairing”, Proceedings of 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2010), Aras, France
4. M. A. Hashmi, A. El Fallah Seghrouchni, “Coordination of Temporal Plans for the Reactive and Proactive Goals”, Proceedings of IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT’ 10), Toronto, Canada
5. Y. Dimopoulos, M. A. Hashmi, P. Moraitis, “Extending SATPLAN to Multiple Agents”, Proceedings of 30th SGAI International Conference on Artificial Intelligence 2010, Cambridge, UK.
6. M. A. Hashmi, A. El Fallah Seghrouchni, “Temporal Planning in Dynamic Environments for P-CLAIM Agents”, In proceedings of Languages, Methodologies and Development Tools for Multi-Agent Systems (LADS’09), Torino, Italy, Springer-Verlag.
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Publications7. M. A. Hashmi, A. El Fallah Seghrouchni, “Temporal Planning in Dynamic
Environments for Mobile Agents”, Proceedings of International Conference on Frontiers of Information Technology (FIT’ 09), Abottabad, Pakistan, Publisher ACM press.
8. M. A. Hashmi, “A Planning Component for CLAIM Agents”, In the proceedings of 17th International Conference on Control Systems and Computer Science, Bucharest, Romania, Volume 2, pages 485-492, Politehnica Press
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Thanks for your attention!!!