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SIF8072 Distributed Artificial Intelligence and Intelligent Agents

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Title: SIF8072 Distributed Artificial Intelligence and Intelligent Agents


1
SIF8072 Distributed Artificial
Intelligence and Intelligent Agents
Lecture 4 Coordination Working Together
  • http//www.idi.ntnu.no/agent/
  • 6 February 2003

Lecturer Sobah Abbas Petersen Email
sap_at_idi.ntnu.no
2
Lecture Outline
  1. Recap from last week CDPS and CNET
  2. Coordination techniques
  3. Common coordination techniques
  4. Coordination based on human teamwork
  5. 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.

7
Cooperative Distributed Problem Solving (CDPS)
1. Problem decomposition
2. Subproblem solution
3. Answer synthesis
Ref Smith Davis, 1980
8
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 1.3
Task 1.1
9
The Contract Net Protocol
I have a problem!
manager
Potential contrators
announcement
(b) Task Announcement
(a) Recognising the problem
manager
manager
Potential contrator
Award task
bids
(c) Bidding
(d) Award Contract
10
..Task Allocation
11
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.

12
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.

13
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

14
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

15
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
16
Alternative Coordination Structures 1 Product
Hierarchy
17
Alternative Coordination Structures 2 Functional
Hierarchy
Product Manager (several products)
18
Alternative Coordination Structures 3 Centralised
Market
Product Manager 3
Product Manager 2
Product Manager 1
Functional Managers
19
Alternative Coordination Structures
4 Decentralised Market
Product Manager 3
Product Manager 2
Product Manager 1
Designers
Salesmen
Engineers
20
Comparison of Organization Structures
Production cost Coordination cost Vulnerability cost
Product hierarchy H L H-
Funtional hierarchy L M- H
Centralised market L M H-
Decentralised market L H L
21
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.

22
Lets 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.

23
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

24
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

26
Partial Global Planning (PGP) 2
  • 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.

27
Partial Global Planning (PGP) 3
  • 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.

28
Partial Global Planning (PGP) 4
  • PGP involves 3 iterated stages
  • Each agent decides what its own goals are and
    generates short-term plans in order to achieve
    them.
  • Agents exchange information to determine where
    plans and goals interact.
  • Agents alter local plans in order to better
    coordinate their own activities.

29
Partial Global Planning (PGP) 5
  • 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.

30
Partial Global Planning (PGP) 6
  • A DAI testbed revisited.

Agenti
Overlapping area
Vehicle track
j
i
Agentj
31
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

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.

33
Multi-agent Planning 2
  • Two basic approaches
  • Centralised plans of individual agents analysed
    by a central coordinator to identify
    interactions.
  • Distributed a group of agents cooperate to form
    a
  • Centralised plan
  • Distributed plan

34
Multi-agent Planning 3
  • 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.

35
Multi-agent Planning 4
  • 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
Multi-agent Planning 5
  • 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.

37
Multi-agent Planning 6
  • Sometime Plans can also become obsolete very
    quickly. i.e. Short life-span.

38
Lets take a minute
  • Can you think of a situation where multi-agent
    planning will not be appropriate?
  • Discuss with your neighbours.

39
Comparing Common Coordination Techniques
40
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

41
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 gt ?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)

43
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

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.

45
Coordination Cooperation 2
  • 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

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.

48
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.

49
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

50
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
  • When to keep a commitment (retain)
  • When to revise a commitment (rectify)
  • When to remove a commitment (abandon)

51
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.

53
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
  • Mutual Modelling
  • Joint Intentions (Jennings)

54
Teamwork Definition
  • American Heritage Dictionary
  • Cooperative effort by the members of a team to
    achieve a common goal.

55
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
56
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.
  • Agents i, j and k are a team and have a common
    goal G.

58
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

59
Joint Intentions (Jennings) Revisited Social
Conventions
  • Team members must be aware of the convention that
    govern their interactions. e.g.
  • 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!

60
Teamwork Model Based on CDPS
  • Recognition
  • Agent has a goal and recognises the potential for
    cooperative action.
  • Team Formation
  • Finds a group of agents that have a commitment to
    joint action.
  • Plan Formation
  • Agree upon course of action, (through a process
    of negotiation).
  • Team Action
  • Execute agreed plan of joint action.

G
61
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.

62
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
Lets take a minute
  • 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
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