Distributed Problem Solving

& Planning

Distributed Problem Solving

& Planning

By: Sachin KambojBy: Sachin Kamboj

CISC 889: Agents, Web Services and Grid ComputingFall 2003

OutlineOutline

• Introduction

• Motivation

• Distributed Problem Solving Strategies– Task Sharing– Result Sharing– Distributed Planning– Distributed Planning and Execution

• Comparison with Web Services and Grid Computing

• Introduction

• Motivation

• Distributed Problem Solving Strategies– Task Sharing– Result Sharing– Distributed Planning– Distributed Planning and Execution

• Comparison with Web Services and Grid Computing

IntroductionIntroduction

• Distributed Problem Solving– Agents work together to solve problems

that require collective effort.– Requires:

• Coherence– Need to want to work together

• Competence– Need to know how to work together

• Distributed Planning– Problem Solving in which the agents work

together to construct a plan.

• Distributed Problem Solving– Agents work together to solve problems

that require collective effort.– Requires:

• Coherence– Need to want to work together

• Competence– Need to know how to work together

• Distributed Planning– Problem Solving in which the agents work

together to construct a plan.

MotivationMotivation

• Resources may be distributed– Using distributed resources concurrently can allow

a speedup of problem solving due to parallelism.

• Resources may be distributed– Using distributed resources concurrently can allow

a speedup of problem solving due to parallelism.

MotivationMotivation

• Problem solving capabilities may be distributed– Separate processes may solve different parts of

the problem.

• Problem solving capabilities may be distributed– Separate processes may solve different parts of

the problem.

Online Auction on EBayOnline Auction on EBay

Product SearchProduct Search Credit Card Billing

Credit Card BillingBid ManagerBid Manager Product

Delivery

ProductDelivery

MotivationMotivation

• Data may be distributed– Example: In distributed sensor networks

• Data may be distributed– Example: In distributed sensor networks

Agent 3

Agent 1 Agent 2

Agent 4

MotivationMotivation

• Results may be distributed– The results may have to be acted upon by multiple

agents.

• Results may be distributed– The results may have to be acted upon by multiple

agents.

Distributed Problem Solving Strategies

Distributed Problem Solving Strategies

• Task Sharing

• Result Sharing

• Distributed Planning

• Distributed Planning and Execution

• Task Sharing

• Result Sharing

• Distributed Planning

• Distributed Planning and Execution

Task SharingTask Sharing

Task SharingTask Sharing

• Task Decomposition– Generate a set of tasks to be passed to others– Decompose large tasks into subtasks that can be

tackled by different agents

• Task Allocation– Assign subtasks to appropriate agents

• Task Accomplishment– Agents accomplish task, either individually or

through further decomposition

• Result Synthesis– Combine the results to generate the final results

• Task Decomposition– Generate a set of tasks to be passed to others– Decompose large tasks into subtasks that can be

tackled by different agents

• Task Allocation– Assign subtasks to appropriate agents

• Task Accomplishment– Agents accomplish task, either individually or

through further decomposition

• Result Synthesis– Combine the results to generate the final results

Task Sharing Example: Towers of Hanoi

Task Sharing Example: Towers of Hanoi

• Task Decomposition:– Means-End Analysis:

• Move the largest disk that is misplaced to its destination peg.• Get to the state where the largest disk can be moved.

– Move the other disks on top of the largest disk to a peg other than the destination of the largest so that the largest can be moved.

• Task Allocation:– Allocate sub-problems to identical agents capable of

solving it.• Task Accomplishment:

– If the recursive sub-problems have same start and goal states terminate.

• Result Synthesis

• Task Decomposition:– Means-End Analysis:

• Move the largest disk that is misplaced to its destination peg.• Get to the state where the largest disk can be moved.

– Move the other disks on top of the largest disk to a peg other than the destination of the largest so that the largest can be moved.

• Task Allocation:– Allocate sub-problems to identical agents capable of

solving it.• Task Accomplishment:

– If the recursive sub-problems have same start and goal states terminate.

• Result Synthesis

Task Sharing Example: Towers of Hanoi

Task Sharing Example: Towers of Hanoi

Task Sharing in Heterogeneous Systems

Task Sharing in Heterogeneous Systems

• Motivation:– Difficult to build agents that are competent in every possible

task.– Will result in agents that are “Jacks of all trades and masters

of none”– Omni-capable agents would only use a small proportion of

their capabilities at any one time.

• Motivation:– Difficult to build agents that are competent in every possible

task.– Will result in agents that are “Jacks of all trades and masters

of none”– Omni-capable agents would only use a small proportion of

their capabilities at any one time.

Car Design AgentCar Design Agent

EngineEngine TransmissionTransmission

AerodynamicsAerodynamicsBrakesBrakes

…………

• Solution:– Build specialist agents that can work together to

solve problems beyond their individual capabilities.– Dynamic combination of heterogeneous agents…

• Solution:– Build specialist agents that can work together to

solve problems beyond their individual capabilities.– Dynamic combination of heterogeneous agents…

Task Sharing in Heterogeneous Systems

Task Sharing in Heterogeneous Systems

Car Design AgentCar Design Agent

Engine DesignAgent

Engine DesignAgent

Transmission Design Agent

Transmission Design Agent

Body Design Agent

Body Design Agent

Brakes Design Agent

Brakes Design Agent

Fuel Injection Design Agent

Fuel Injection Design Agent

Cylinder Design Agent

Cylinder Design Agent

Task Sharing in Heterogeneous Systems

Task Sharing in Heterogeneous Systems

• Problem:– How can an agent that has to solve a problem find

the “specialists”?

• Naïve Solution:– Have a table that identifies the capabilities of the

agents.– Use table to select appropriate agents and give

them the sub-problem.– Problems:

• How is selection done if several candidate agents exist?

• Problem:– How can an agent that has to solve a problem find

the “specialists”?

• Naïve Solution:– Have a table that identifies the capabilities of the

agents.– Use table to select appropriate agents and give

them the sub-problem.– Problems:

• How is selection done if several candidate agents exist?

Task Sharing in Heterogeneous Systems Selection of Agents

Task Sharing in Heterogeneous Systems Selection of Agents

• Announce a sub-problem to specific agents.• Request return bids describing

acceptance/availability• Assign sub-problem to the highest bidder• Problem:

– What if no agents bid for a sub-problem?

• Solution: Use one of the following:– Broadcast Contracting– Retry– Announcement Revision– Alternative Decomposition

• Announce a sub-problem to specific agents.• Request return bids describing

acceptance/availability• Assign sub-problem to the highest bidder• Problem:

– What if no agents bid for a sub-problem?

• Solution: Use one of the following:– Broadcast Contracting– Retry– Announcement Revision– Alternative Decomposition

Result SharingResult Sharing

Results SharingResults Sharing

• Motivation:– Results of different problem solvers on the same

task may differ…– Sharing results can improve group performance by

increasing:• Confidence• Completeness• Precision• Timeliness

• Agents need to know how to assimilate results from different agents– Assimilation may be non-trivial/costly/require large

amounts of communication and incur overheads in management.

• Motivation:– Results of different problem solvers on the same

task may differ…– Sharing results can improve group performance by

increasing:• Confidence• Completeness• Precision• Timeliness

• Agents need to know how to assimilate results from different agents– Assimilation may be non-trivial/costly/require large

amounts of communication and incur overheads in management.

• Agents lack information about what is happening elsewhere– This information might impact their calculations.– As a result agents form tentative results that might

be unnecessary

• Functionally Accurate:– Agents get the answer eventually– But with many possible false starts

• Cooperative:– Iterative exchange between agents is required

• Agents lack information about what is happening elsewhere– This information might impact their calculations.– As a result agents form tentative results that might

be unnecessary

• Functionally Accurate:– Agents get the answer eventually– But with many possible false starts

• Cooperative:– Iterative exchange between agents is required

Functionally Accurate Cooperation

Functionally Accurate Cooperation

Functionally Accurate Cooperation

Functionally Accurate Cooperation

• Advantages:– Exchanging tentative partial solutions can

impact:• Completeness

– Partial solutions are synthesized into larger solutions that solve more of the overall solution

• Precision– Agents may use results from other agents to refine

their own solutions

• Confidence– Confidence measures can be combined to increase

overall confidence in the solution.

• Advantages:– Exchanging tentative partial solutions can

impact:• Completeness

– Partial solutions are synthesized into larger solutions that solve more of the overall solution

• Precision– Agents may use results from other agents to refine

their own solutions

• Confidence– Confidence measures can be combined to increase

overall confidence in the solution.

Functionally Accurate Cooperation

Functionally Accurate Cooperation

• Problems:– Iterative exchange of partial results should lead to

some agent having enough information to keep moving the overall problem solving forward

– A large amount of communication overhead is involved in the sharing of partial results.

– May result in wasted computations– May lead to Distraction:

• Agents gravitate towards doing the same problem solving actions.

• Problems:– Iterative exchange of partial results should lead to

some agent having enough information to keep moving the overall problem solving forward

– A large amount of communication overhead is involved in the sharing of partial results.

– May result in wasted computations– May lead to Distraction:

• Agents gravitate towards doing the same problem solving actions.

Shared RepositoriesMotivation

Shared RepositoriesMotivation

• Sharing partial results involves a lot of communication overhead

• Sharing partial results involves a lot of communication overhead

Agent 1Agent 1

Agent 2Agent 2

Agent 3Agent 3

Agent 4Agent 4

Agent 5Agent 5

Agent 6Agent 6

Agent 7Agent 7

Agent 9Agent 9Agent 8Agent 8

Shared RepositoriesShared Repositories

• Instead of agents multicasting partial results to all other agents, the results are stored in a central repository– Also known as a blackboard

• Instead of agents multicasting partial results to all other agents, the results are stored in a central repository– Also known as a blackboard

Agent 1Agent 1

Agent 2Agent 2

Agent 3Agent 3 Agent 4Agent 4

Agent 5Agent 5

Agent 6Agent 6

Agent 7Agent 7

Agent 9Agent 9

Agent 8Agent 8

Shared Repository

Shared RepositoriesShared Repositories

• Support search through alternative designs– Agents with different design criteria can

revise and critique the alternatives• Similar to a distributed constraint satisfaction

problem

• Support search through alternative designs– Agents with different design criteria can

revise and critique the alternatives• Similar to a distributed constraint satisfaction

problem

Negotiated SearchNegotiated Search

• Negotiation:– An agent generates a proposal and other agents review it:

• If an agent does not like the proposal, it rejects it– Agents might also generate counter-proposals

– Proposals are reviewed iteratively

• Negotiated Search:– A use of shared repositories– Agents concurrently investigate multiple solution paths– Conflicts between solution are resolved using negotiation– Agents have operators for initiating solutions, extending

solutions, critiquing solutions and relaxing solution requirements

• Negotiation:– An agent generates a proposal and other agents review it:

• If an agent does not like the proposal, it rejects it– Agents might also generate counter-proposals

– Proposals are reviewed iteratively

• Negotiated Search:– A use of shared repositories– Agents concurrently investigate multiple solution paths– Conflicts between solution are resolved using negotiation– Agents have operators for initiating solutions, extending

solutions, critiquing solutions and relaxing solution requirements

Distributed Constrained Heuristic Search

Distributed Constrained Heuristic Search

• Used to handle distributed resource allocation• An agent is associated with each resource• DCHS works as follows:

– Agent begins with a problem topology– Agent determines resource requirement and computes a

demand profile– Agent sends profile to the resource– Resource computes aggregate demand and informs the

agents making the demands– Agent uses aggregate demand to identify preferred resource– Agent requests the resource to reserve the specified interval– Resource grants reservation if possible– Agent processes the response from the resource

• Used to handle distributed resource allocation• An agent is associated with each resource• DCHS works as follows:

– Agent begins with a problem topology– Agent determines resource requirement and computes a

demand profile– Agent sends profile to the resource– Resource computes aggregate demand and informs the

agents making the demands– Agent uses aggregate demand to identify preferred resource– Agent requests the resource to reserve the specified interval– Resource grants reservation if possible– Agent processes the response from the resource

Organizational StructuringOrganizational Structuring

• An alternative strategy for reducing communication overhead

• Exploits the task decomposition structure/ organizational structure of agents– Define roles, resources and preferences for the agents– Associates with each agent the type of tasks that it can do

• Agents need to be informed of partial results that can influence how they carry out their responsibilities– Agents need not be told of results that do not affect their

actions

• Make use of stored pattern-response rules

• An alternative strategy for reducing communication overhead

• Exploits the task decomposition structure/ organizational structure of agents– Define roles, resources and preferences for the agents– Associates with each agent the type of tasks that it can do

• Agents need to be informed of partial results that can influence how they carry out their responsibilities– Agents need not be told of results that do not affect their

actions

• Make use of stored pattern-response rules

Communication StrategiesCommunication Strategies

• Concerned with timing issues• Agents need to know not only what results to

send but also when to send them• Sending a result too soon can:

– Clutter up the recipient memory– Distract the recipient away from useful work

• Failure to send results can:– Lead to delays– Result in a failure of the system to converge on

reasonable solutions

• Concerned with timing issues• Agents need to know not only what results to

send but also when to send them• Sending a result too soon can:

– Clutter up the recipient memory– Distract the recipient away from useful work

• Failure to send results can:– Lead to delays– Result in a failure of the system to converge on

reasonable solutions

Communication StrategiesCommunication Strategies

• When possibility of cluttering memory is short:– Send all partial results

• If exchange of partial results leads a subset of agents into redundant exploration of a part of the solution space:– Refrain from sending partial results

• Alternatively, only send results when requested– Especially when space of results formed is large

and only a few are needed by others.

• When possibility of cluttering memory is short:– Send all partial results

• If exchange of partial results leads a subset of agents into redundant exploration of a part of the solution space:– Refrain from sending partial results

• Alternatively, only send results when requested– Especially when space of results formed is large

and only a few are needed by others.

Distributed PlanningDistributed Planning

Traditional PlanningTraditional Planning

• Set of states– Conjunction of terms that are true about the world

• Set of operators– Preconditions

• Things that have to be true about the world to apply the operator

– Effects• Changes to the current state (Add List, Delete List)

• Goals• Planning is a backward search from the goal

state to the start state– At each step, the agent tries to find an operator

whose effect achieves a goal or a precondition of another operator that has already been applied

• Set of states– Conjunction of terms that are true about the world

• Set of operators– Preconditions

• Things that have to be true about the world to apply the operator

– Effects• Changes to the current state (Add List, Delete List)

• Goals• Planning is a backward search from the goal

state to the start state– At each step, the agent tries to find an operator

whose effect achieves a goal or a precondition of another operator that has already been applied

Traditional Planning(Example)

Traditional Planning(Example)

FinishFinish

Have (Drill), Have (Milk), Have (Bananas), At (Home)

Buy (Drill)Buy (Drill)

At (HWS), Sells (HWS, Drill)

Buy (Milk)Buy (Milk)

At (SM), Sells (SM, Milk)

Buy (Bananas)Buy (Bananas)

At (SM), Sells (SM, Bananas)

Go (HWS)Go (HWS)

At (x)

Go (SM)Go (SM)

At (x)

StartStart

At (Home), Sells (SM, Bananas), Sells (SM, Milk), Sells (HW, Drill)

Go (HWS)Go (HWS)

At (Home)

Go (SM)Go (SM)

At (HWS)

Traditional Planning(Simplifying Assumptions)

Traditional Planning(Simplifying Assumptions)

• Planning agent is omniscient• The action that the agents can perform have definite

outcomes• The goals presented to the agents are categorical

– They are either achieved or not– There is no partial satisfaction of goals

• The agent is the only source of change in the environment

• The goals presented remain unchanged throughout the process of planning and execution

• Actions do not have temporal extent or duration of occurrence

• Planning agent is omniscient• The action that the agents can perform have definite

outcomes• The goals presented to the agents are categorical

– They are either achieved or not– There is no partial satisfaction of goals

• The agent is the only source of change in the environment

• The goals presented remain unchanged throughout the process of planning and execution

• Actions do not have temporal extent or duration of occurrence

Traditional Planning(Consequences of Underlying Assumptions)

Traditional Planning(Consequences of Underlying Assumptions)

• There is no need to interleave planning and execution– Because agent has all the knowledge it needs at

planning time

• No need for re-planning – A plan can always be executed successfully

• Planning problems do not compete with each other for the planning agent’s attention.– Goals are all known at the outset and remain fixed

throughout planning and execution

• Assumptions fail to hold for realistic situations in which planning is applied…

• There is no need to interleave planning and execution– Because agent has all the knowledge it needs at

planning time

• No need for re-planning – A plan can always be executed successfully

• Planning problems do not compete with each other for the planning agent’s attention.– Goals are all known at the outset and remain fixed

throughout planning and execution

• Assumptions fail to hold for realistic situations in which planning is applied…

Distributed Planning(Example Scenario)

Distributed Planning(Example Scenario)

• You have a project due on Monday– You decide to work on it over the weekend

• Exact time is not fixed: will depend on other plans

– Tentative Plans:• Saturday Morning: Run errands• Saturday Night: Watch ‘The Matrix’• Sunday: Finish off project

– Saturday Afternoon• Receive an invitation from a friend to her birthday party on

Sunday evening

– Revise Plans:• Saturday Morning: Run errands• Saturday Evening/Night: Work on Project• Sunday Morning: Finish off project• Sunday Night: Go to friends birthday party

• You have a project due on Monday– You decide to work on it over the weekend

• Exact time is not fixed: will depend on other plans

– Tentative Plans:• Saturday Morning: Run errands• Saturday Night: Watch ‘The Matrix’• Sunday: Finish off project

– Saturday Afternoon• Receive an invitation from a friend to her birthday party on

Sunday evening

– Revise Plans:• Saturday Morning: Run errands• Saturday Evening/Night: Work on Project• Sunday Morning: Finish off project• Sunday Night: Go to friends birthday party

Distributed PlanningDistributed Planning

• Aims to overcome some of the limitations of traditional planning– Handle the example scenario just presented

• Specialization of distributed problem solving, where the problem to be solved is to design a plan

• Distributed means:– Planning is distributed– Plans are distributed– Both planning and plans are distributed

• Aims to overcome some of the limitations of traditional planning– Handle the example scenario just presented

• Specialization of distributed problem solving, where the problem to be solved is to design a plan

• Distributed means:– Planning is distributed– Plans are distributed– Both planning and plans are distributed

Distributed PlanningCentralized Planning for Distributed Plans

Distributed PlanningCentralized Planning for Distributed Plans

• Formulation of plans that are to be executed in a distributed fashion in a centralized manner

• Example:– A partial order planner can generate plans that do not have

a strict ordering between actions• Such actions can be executed in parallel by multiple agents

• Algorithm:– Generate a partial order plan– Decompose plan into sub-plans– Insert synchronization actions into sub-plans– Allocate sub-plan to agents using task passing mechanisms– Initiate plan execution and monitor progress

• Algorithm is a specialization of task sharing

• Formulation of plans that are to be executed in a distributed fashion in a centralized manner

• Example:– A partial order planner can generate plans that do not have

a strict ordering between actions• Such actions can be executed in parallel by multiple agents

• Algorithm:– Generate a partial order plan– Decompose plan into sub-plans– Insert synchronization actions into sub-plans– Allocate sub-plan to agents using task passing mechanisms– Initiate plan execution and monitor progress

• Algorithm is a specialization of task sharing

Distributed PlanningDistributed Planning for Centralized Plans

Distributed PlanningDistributed Planning for Centralized Plans

• Formulating a complex plan may require collaboration among a variety of cooperative planning specialists

• Similar to task-sharing and result-sharing problem solving– Overall problem formulation task is decomposed

and distributed among various planning specialists• Each planning specialist generates its portion of the plan• Interaction between specialists might be through the

exchange of partially specified plans

• Example:– Machining of tools in the manufacturing domain

• Formulating a complex plan may require collaboration among a variety of cooperative planning specialists

• Similar to task-sharing and result-sharing problem solving– Overall problem formulation task is decomposed

and distributed among various planning specialists• Each planning specialist generates its portion of the plan• Interaction between specialists might be through the

exchange of partially specified plans

• Example:– Machining of tools in the manufacturing domain

Distributed PlanningDistributed Planning for Distributed Plans

Distributed PlanningDistributed Planning for Distributed Plans

• Both the planning process and its results are distributed

• Multi-agent plans are not represented in entirety anywhere in the system– No one agent knows the complete plan– Distributed pieces of a plan should be compatible

• Techniques that can be used:– Plan merging– Iterative plan formation– Negotiation in distributed planning

• Both the planning process and its results are distributed

• Multi-agent plans are not represented in entirety anywhere in the system– No one agent knows the complete plan– Distributed pieces of a plan should be compatible

• Techniques that can be used:– Plan merging– Iterative plan formation– Negotiation in distributed planning

Distributed Planning for Distributed PlansPlan Merging

Distributed Planning for Distributed PlansPlan Merging

• Each agent formulates its own plan individually• Agents work together to ensure that their separate

plans can be executed without conflict– Local plans are exchanged– Messaging and/or timing commitments are imposed to

resolve negative plan interactions

• Centralized plan coordination approach– An agent collects together the individual plans– Agent analyzes the plans to discover the sequence of

actions that leads to a conflict• Reachability Analysis

– Enumerate all possible states of the world that can be reached from the initial state by executing actions

• Plans are modified to remove conflicts

• Each agent formulates its own plan individually• Agents work together to ensure that their separate

plans can be executed without conflict– Local plans are exchanged– Messaging and/or timing commitments are imposed to

resolve negative plan interactions

• Centralized plan coordination approach– An agent collects together the individual plans– Agent analyzes the plans to discover the sequence of

actions that leads to a conflict• Reachability Analysis

– Enumerate all possible states of the world that can be reached from the initial state by executing actions

• Plans are modified to remove conflicts

Distributed Planning for Distributed PlansIterative Plan Formation

Distributed Planning for Distributed PlansIterative Plan Formation

• Not all problems allow agents to develop their own local plans

• Require agents to search through larger spaces of plans rather than each proposing a single specific plan– Each agent might construct a set of all feasible

plans – The planning process consists of fitting together

subsets of agents plans• Example methods:

– Plan Combination Search– Distributed Hierarchical Planning

• Not all problems allow agents to develop their own local plans

• Require agents to search through larger spaces of plans rather than each proposing a single specific plan– Each agent might construct a set of all feasible

plans – The planning process consists of fitting together

subsets of agents plans• Example methods:

– Plan Combination Search– Distributed Hierarchical Planning

Distributed Planning and Execution

Distributed Planning and Execution

• Plan merging:– Each agent individually builds a plan.– Individual plans are coordinated– Results of coordination are executed.

• If one or more of the agents should fail in carrying out their plans, the whole coordinated plan set might fail

• Post-planning coordination– Done after one or more plans fail– Methods:

• Contingency planning– Each agent formulates alternative plans to respond to possible

contingencies

• Monitoring and replanning– If there is a deviation from a plan stop all execution and repeat the

plan-coordinate-execute cycle

• Plan merging:– Each agent individually builds a plan.– Individual plans are coordinated– Results of coordination are executed.

• If one or more of the agents should fail in carrying out their plans, the whole coordinated plan set might fail

• Post-planning coordination– Done after one or more plans fail– Methods:

• Contingency planning– Each agent formulates alternative plans to respond to possible

contingencies

• Monitoring and replanning– If there is a deviation from a plan stop all execution and repeat the

plan-coordinate-execute cycle

Distributed Planning and ExecutionPre-planning coordination

Distributed Planning and ExecutionPre-planning coordination

• Coordination is done in such a way that no matter what happens during execution they agents will remain coordinated with each other

• Introduces coordination restriction– Such restrictions usually take the form of social laws

• A prohibition against particular choices of actions in particular contexts

• For example, entering an intersection on a red light

– Restrictions are derived by working back from undesirable states:

• Combination of actions that lead to those states are found• Restrictions are imposed so that those actions cannot

occur

• Coordination is done in such a way that no matter what happens during execution they agents will remain coordinated with each other

• Introduces coordination restriction– Such restrictions usually take the form of social laws

• A prohibition against particular choices of actions in particular contexts

• For example, entering an intersection on a red light

– Restrictions are derived by working back from undesirable states:

• Combination of actions that lead to those states are found• Restrictions are imposed so that those actions cannot

occur

Distributed Planning and ExecutionInterleaving Planning, Coordination & ExecutionDistributed Planning and ExecutionInterleaving Planning, Coordination & Execution

• In certain situations, it might be impractical to distinguish between planning and execution– The environment may be highly dynamic

• Assumptions made by the agent while planning may no longer be true

– A single agent cannot decide on the future course of action without exchanging info with other agents

• Example situations which required interleaving:– Distributed sensor networks– Dynamic resource allocation

• May make use of partial global planning

• In certain situations, it might be impractical to distinguish between planning and execution– The environment may be highly dynamic

• Assumptions made by the agent while planning may no longer be true

– A single agent cannot decide on the future course of action without exchanging info with other agents

• Example situations which required interleaving:– Distributed sensor networks– Dynamic resource allocation

• May make use of partial global planning

Comparison with Web Services and Grid Computing

Comparison with Web Services and Grid Computing

• Method of distributed problem solving depends on the application– Includes a large variety of techniques– There is no one “standard” or silver bullet.

• Same methods may be used in web services and grid computing– Distributed constrained heuristic search may be

used in grid computing for the allocation of resources

– Web services choreography is essentially a form of planning with preconditions, post-conditions, triggering events and flows

• Method of distributed problem solving depends on the application– Includes a large variety of techniques– There is no one “standard” or silver bullet.

• Same methods may be used in web services and grid computing– Distributed constrained heuristic search may be

used in grid computing for the allocation of resources

– Web services choreography is essentially a form of planning with preconditions, post-conditions, triggering events and flows

ReferencesReferences

• Edmund H. Durfee: “Distributed Problem Solving and Planning”, in Chapter 3, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, Gerhard Weiss (Editor)

• Edmund H. Durfee: “Distributed Problem Solving and Planning”, in Chapter 3, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, Gerhard Weiss (Editor)

Thank You!!Thank You!!