sif8072 distributed artificial intelligence and intelligent agents

SIF8072 Distributed Artificial Intelligence

andIntelligent Agents

http://www.idi.ntnu.no/~agent/6 February 2003

Lecture 4: CoordinationWorking Together

Lecturer: Sobah Abbas Petersen

Email: [email protected]

http://www.idi.ntnu.no/~agent/









2

Lecture Outline

1. Recap from last week – CDPS and CNET

2. Coordination techniques

1. Common coordination techniques

2. Coordination based on human teamwork

3. Teamwork

3

References - Curriculum

• Wooldridge: ”Introduction to MAS”,

– Chapter 9, chapter 4

• N. R. Jennings. ”Coordination Techniques for

Distributed Artificial Intelligence”, in: G. M. P. O'Hare,

N. R. Jennings (eds). Foundations of Distributed

Artificial Intelligence, John Wiley & Sons, 1996, pp.

187-210.

4

References – Recommended Reading

• Not curriculum:

– E. H. Durfee, ”Distributed Problem Solving and Planning”, in

Multiagent Systems (G. Weiß ed.), MIT Press, Cambridge, MA.,

1999, pp. 121-164.

– H. Nwana, L. Lee, N. R. Jennings. ”Coordination in Software Agent

Systems”, The British Telecom Technical Journal, Vol. 14, No. 4,

1996, pp. 79-88.

– R. Davis and R. G. Smith, ”Negotiation as a Metaphor for Distributed

Problem Solving”, (A. H. Bond and L. Gasser eds.) Readings in

Distributed Artificial Intelligence, Morgan Kaufmann Publishers, 1988,

pp. 333-356.

5

Coordination

”The process by which an agent reasons about its

local actions and the (anticipated) actions of

others to try and ensure that the community acts

in a coherent manner.”

Jennings,1996

6

Coordination Example

Consider an interaction between two robots, A and B,

operating in a warehouse. The robots have been designed

by different companies, and they are stacking and

unstacking boxes to remove certain goods that have been

stored in the building. They need to coordinate their

actions to share the work load and to avoid knocking

into each other and dropping the boxes.

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Cooperative Distributed Problem Solving (CDPS)

1.Problem

decomposition

2.Subproblem

solution

3.Answer synthesis

Ref: Smith & Davis, 1980

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Task and Result Sharing

• Task sharing:

– when a problem is decomposed

into subproblems and allocated

to different agents.

• Result sharing:

– When agents share information

relevant to their subproblems.

Task 1

Task 1.2 Task 1.3Task 1.1

A1 A2 A3

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The Contract Net Protocol

I have a problem!

(a) Recognising the problem

managerPotentialcontrators

announcement

(b) Task Announcement

manager

bids

(c) Bidding

manager

Award task Potentialcontrator

(d) Award Contract

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…..Task Allocation

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Result Sharing

• Problem solving proceeds by agents cooperatively

exchanging information as the solution is developed.

• Results may be shared:

– proactively - one agent sends another agent some information

because it believes that the other will be interested in it.

– reactively – an agent sends information to another in response to a

request.

A1 A2 A3

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The Coordination Problem

• Managing the

interdependencies between the

activities of agents. e.g.

– You and I both want to leave the

room. We independently walk

towards the door, which can only

fit one of us. I graciously permit

you to leave first.

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Coordination Techniques

• Organisational Structures

• Meta-level Information Exchange

– e.g. Partial Global Planning (PGP), (Durfee)

• Multi-agent Planning

• Norms and social laws

• Coordination Models based on human teamwork:

– Joint commitments (Jennings)

– Mutual Modelling

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Organizational Structures

• A pattern of information and control relationships between

individuals.

• Responsible for shaping the types of interactions among the agents.

• Aids coordination by specifying which actions an agent will

undertake.

• Organisational structures may be:

– Functional

– Spatial

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Organizational Structure Models

• A pattern for decision-making and communication

among a set of agents who perform tasks in order

to achieve goals. e.g.

– Automobile industry

• Has a set of goals: To produce different lines of cars

• Has a set of agents to perform the tasks: designers, engineers,

salesmen

Reference: Malone 1987

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Alternative Coordination Structures 1Product Hierarchy

Designer

Product Manager I

SalesmanEngineer Designer

Product Manager 2

SalesmanEngineer

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Product Manager (several products)

Alternative Coordination Structures 2Functional Hierarchy

Designers

DesignManager

Salesmen

SalesManager

Engineers

EngineeringManager

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Alternative Coordination Structures 3Centralised Market

Product Manager 2

Designers

DesignManager

Salesmen

SalesManager

Engineers

EngineeringManager

Product Manager 1 Product Manager 3

FunctionalManagers

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Alternative Coordination Structures 4Decentralised Market

Product Manager 2

Designers SalesmenEngineers

Product Manager 1 Product Manager 3

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Comparison of Organization Structures

Production

cost

Coordination

cost

Vulnerability

cost

Product

hierarchyH L H-

Funtional

hierarchyL M- H+

Centralised

marketL M+ H-

Decentralised

marketL H L

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Organizational Structures - Critique

• Useful when there are master/slave relationships in the

MAS.

• Control over the slaves actions – mitigates against benefits

of DAI such as reliability, concurrency.

• Presumes that atleast one agent has global overview – an

unrealistic assumption in MAS.

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Let’s take a minute……

• Can you think of a situation in your everyday life

where organisation structures are a way of

coordinating your activities?

• Discuss with your neighbours.

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Meta-level Information Exchange

e.g. Partial Global Planning (PGP), (Durfee)






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Meta-level Information Exchange

• Exchange control level information about current priorities

and focus.

• Control level information

– May change

– Influence the decisions of agents

• Does not specify which goals an agent will or will not

consider.

• Imprecise

• Medium term – can only commit to goals for a limited

amount of time.

25

Partial Global Planning (PGP) 1

• A DAI testbed – Distributed Vehicle Monitoring Testbed

(DVMT) – to successfully track a number of vehicles that

pass within the range of a set of distributed sensors

(agents).

• Each agent monitors a

dedicated area

• There could be overlapping

areas

Overlappingarea

Agenti

Agentj

Vehicletrack

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• Main principle: cooperating agents exchange information

in order to reach common conclusions about the problem

solving process.

• Why is planning partial?

– The system does not generate a plan for the entire problem.

• Why is planning global?

– Agents form non-local plans by exchanging local plans and

cooperating to achieve a non-local view of problem solving.

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• Starts with the premise that tasks are inherently decomposed.

• Assumes that an agent with a task to plan for might be unaware as

to what tasks other agents might be planning for and how those

tasks are related to its own.

• No individual agent might be aware of the global tasks or states.

• Purpose of coordination is to develop sufficient awareness.

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• PGP involves 3 iterated stages:

1. Each agent decides what its own goals are and

generates short-term plans in order to achieve them.

2. Agents exchange information to determine where

plans and goals interact.

3. Agents alter local plans in order to better coordinate

their own activities.

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• Partial Global Plan: a cooperatively generated

datastructure containing the actions and interactions of a

group of agents.

• Contains:

– Objective – the larger goal of the system.

– Activity map – what agents are actually doing and the results

generated by the activities.

– Solution construction graph – a representation of how the agents

ought to interact in order to successfully generate a solution.

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• A DAI testbed – revisited.

Overlappingarea

Agenti

Agentj

Vehicletrack

ji

31





Multi-agent Planning





32

Multi-agent Planning 1

• Agents generate, exchange and synchronise explicit plans

of actions to coordinate their joint activity.

• They arrange apriori precisely which tasks each agent will

take on.

• Plans specify a sequence of actions for each agent.

• It is a trade-off between specificity and reactive.

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• Two basic approaches:

1. Centralised – plans of individual agents analysed by a

central coordinator to identify interactions.

2. Distributed – a group of agents cooperate to form a:

1. Centralised plan

2. Distributed plan

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• Distributed Planning for centralised plans:

– e.g. Air traffic control domain (Cammarata)

• Aim: Enable each aircraft to maintain a flight plan that will

maintain a safe distance with all aircrafts in its vicinity.

• Each aircraft send a central coordinator information about its

intended actions. The coordinator builds a plan which specifies

all of the agents’ actions including the ones that they should

take to avoid collision.

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• Distributed Planning for distributed

plans:

– Individual plans of agents, coordinated dynamically.

– No individual with a complete view of all the agents’

actions.

– More difficult to detect and resolve undesirable

interactions.

36


• Critique:

– Agents share and process a huge amount of information.

– Requires more computing and communication resources.

• Difference between multi-agent planning and PGP:

– PGP does not require agents to reach mutual agreements

before they start acting.

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• Sometime Plans can also become obsolete very quickly.

i.e. Short life-span.

38


• Can you think of a situation where multi-agent

planning will not be appropriate?

• Discuss with your neighbours.

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Comparing Common Coordination Techniques

Organisation

Structures

Meta-level

Information

Exchange

Multi-agent

Planning

low low less

high high more

Predictability

Reactivty

Info

Exchange

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Norms and social laws




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Social Norms and Laws 1

• Norm: an established, expected pattern of behaviour.

– e.g. To queue when waiting for the bus (not always in Norway!!)

• Social laws: similar to Norms, but carry some authority.

– e.g. Traffic rules.

• Social laws in an agent system can be defined as a set of constraints:

– Constraint => E’, ,

• E’ E is a set of environment states

Ac is an action, (Ac is the finite set of actions possible for an agent)

if the environment is in some state e E’, then the action is forbidden.

42

Social Norms and Laws 2

• Example: Feature

interaction in

telecommunications

• Uses deontic logic

(model obligations)

Process incoming

call

Incomingcall screening

Incomingcall answer

Forwardcall

Acceptcall

Recall

Forward #1 Forward #1

obliged obliged

obliged

obliged

forbidden forbidden

forbidden

obliged

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Coordination Models based on human teamwork:



44

Coordination & Cooperation 1

• Can we have coordination without

cooperation?

– ”A group of people are sitting in a park. As a

result of a sudden downpour, all of them run to

a tree in the middle of the park because it is the

only source of shelter.”

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• How does an individual intention towards a goal

differ from being a part of a team (a collective

intention towards a goal)?

Responsibility

– e.g. You and I are lifting a heavy object.

Individual goal team responsibility

Coordination & Cooperation 2

46

Coordination Based on Human Teamwork

• Some agent coordination models are inspired by human

teamwork models, e.g. Joints intentions (Jennings).

• Intentions are central to the concept of practical reasoning.

Practical reasoning = deliberation + means-end reasoning

– Deliberation – deciding what state of affairs to achieve

– Means-end reasoning – deciding how to achieve these states of

affairs

47

Mutual Modelling

• Build a model of the other agents – their beliefs

and intentions.

Put ourselves in the place of the other

• Coordinate own activities based on this model.

• Coordination without cooperation – game-thoery

can be used.

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Joint Intentions

• Proposed by Jennings

• Based on human teamwork models

– ”When a group of agents are engaged in a cooperative activity,

they must have a joint commitment to the overall aim as well as

their individual commitments.”

• Distinguishes between the commitment that underpins an

intention and the associated convention.

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Joint Commitments

• Commitment – a pledge or promise (e.g. to lift the heavy

object).

– Commitment persists – if an agent adopts a commitment, it is not

dropped until for some reason it becomes redundant.

– Commitments may change over time, e.g. due to a change in the

environment

– Main problem with joint commitment:

• Hard to be aware of each others states at all times

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Conventions

• Convention – means of monitoring a commitment

– e.g. specifies under what circumstances a commitment can be

abandoned.

• Need conventions to describe when to change a

commitment:

1. When to keep a commitment (retain)

2. When to revise a commitment (rectify)

3. When to remove a commitment (abandon)

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Convention - Example

• Reasons for terminating a Commitment:

– Commitment Satisfied

– Commitment Unattainable

– Motivation for commitment no longer present

• Rule R1:– If Commitment Satisfied OR

Commitment Unattainable OR

Motivation for Commitment no longer present

then

terminate Commitment.

52

Social Conventions

• Conventions describe how an agent should monitor its

commitments, but not how it should behave towards other

agents.

– Asocial

– Sufficient for goals that are independent.

• For inter-dependent goals:

– Need social conventions

• Specify how to behave with respect to the other members of the team.

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Coordination Summary

• CDPS: Task and result-oriented

– Task-oriented: Contract Net Protocol

• Coordination Techniques:

– Organisational structures

– Meta-level information exchange

• e.g. Partial Global Planning

– Multi-agent Planning

– Social norms and laws


– Joint Intentions (Jennings)

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Teamwork Definition

• American Heritage Dictionary

– Cooperative effort by the members of a

team to achieve a common goal.

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Teamwork Example

Two vehicles travelling in a convoy:

Consider two agents Bob and Alice. Bobs wants to drive

home, but does not know his way. He knows that Alice is

going near there and that she does know the way. Bob

talks to Alice and they both agree that he follows her

through traffic and that they drive together.

Ref: Cohen & Levesque, 1991

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Teamwork 1

• Important distinction:

– Coordinated action that is not cooperative, e.g

• Individual drivers in traffic following traffic rules

– Coordinated cooperative action, e.g

• A convoy of drivers

57

Teamwork 2

• How does an individual intention towards a particular

goal differ from being a part of a team with a

collective intention towards a goal?

– Responsibility towards the other members of the team.G

g2 g3g1

i j k

• Agents i, j and k are a team and have a

common goal G.

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Teamwork 3

• Joint action by a team involves more than just the

union of simultaneous individual actions.

- Joint intentions and mutual beliefs (Cohen &

Levesque, 1991)

- Joint commitment (Jennings, 1996)

• When a group of agents are engaged in a cooperative

activity, they must have:

• Joint commitment to the overall activity

• Individual commitment to the specific task that they have been

assigned to

G

g2 g3g1

i j k

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Joint Intentions (Jennings) RevisitedSocial Conventions

• Team members must be aware of the convention that govern

their interactions. e.g.

G

g1 g2AND

Ai Aj

G

g1 g2OR

Ai Aj

• Both Ai and Aj must fulfill their commitments

to achieve G.

• Either Ai or Aj must fulfill their commitment.

There is a need for all agents in a team to

inform other members of the status of their

commitments!

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Teamwork Model Based on CDPS

1. Recognition

• Agent has a goal and recognises the potential for cooperative

action.

2. Team Formation

• Finds a group of agents that have a commitment to joint action.

3. Plan Formation

• Agree upon course of action, (through a process of negotiation).

4. Team Action

• Execute agreed plan of joint action.

G

G

g2 g3g1

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Team Selection

• ”The process of selecting a group of agents that

have complimentary skills to achieve a given

goal(s).” (Ref: Tidhar et. al., 1996)

– Agents exchange their skills, goals, plans,

current beliefs.

– Done at runtime.

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References – Recommended Reading for Teamwork

• Not curriculum:

– Cohen, P. R. and Levesque, H. J., ”Teamwork”, Nous, 25, 1991.

– Tambe, M., ”Towards Flexible Teamwork”, Journal of

Artificial Intelligence Research, Volume 7, 1997, pp. 83-124.

63


• Discuss with your neighbour an example of

teamwork.

64

Next Lecture: Agent Communication

Will be based on:

”Communication”,

Chapter 8 in

Wooldridge: ”Introduction to MultiAgent

Systems”

sif8072 distributed artificial intelligence and intelligent agents

Documents

problem decomposition2

coordination exampleconsider

coordination problemmanaging

information relevant

agent reasons

software agent systems

different agents

activities of agents