1. SIF8072 Distributed Artificial Intelligence and Intelligent Agents http://www.idi.ntnu.no/~agent/ 6 February 2003 Lecture 4: Coordination Working Together Lecturer: Sobah Abbas Petersen Email: sap@idi.ntnu.no

2. 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. 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. 4 References – Recommended Reading Not curriculum: –E. H. Durfee, ”Distributed Problem Solving and Planning”, in Multiagent Systems (G. Wei ß 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. 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. 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. 7 Cooperative Distributed Problem Solving (CDPS) 1. Problem decomposition 2. Subproblem solution 3. Answer synthesis Ref: Smith & Davis, 1980

8. 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 1.2Task 1.3Task 1.1 A1A2A3

9. 9 The Contract Net Protocol I have a problem! (a) Recognising the problem manager Potential contrators announcement (b) Task Announcement manager bids (c) Bidding manager Award task Potential contrator (d) Award Contract

10. 10 …..Task Allocation

11. 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. A1A2A3

12. 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. 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. 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. 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. 16 Alternative Coordination Structures 1 Product Hierarchy Designer Product Manager I SalesmanEngineer Designer Product Manager 2 Salesman Engineer

17. 17 Product Manager (several products) Alternative Coordination Structures 2 Functional Hierarchy Designers Design Manager Salesmen Sales Manager Engineers Engineering Manager

18. 18 Alternative Coordination Structures 3 Centralised Market Product Manager 2 Designers Design Manager Salesmen Sales Manager Engineers Engineering Manager Product Manager 1 Product Manager 3 Functional Managers

19. 19 Alternative Coordination Structures 4 Decentralised Market Product Manager 2 DesignersSalesmen Engineers Product Manager 1 Product Manager 3

20. 20 Comparison of Organization Structures Production cost Coordination cost Vulnerability cost Product hierarchy HLH- Funtional hierarchy LM-H+ Centralised market LM+H- Decentralised market LHL

21. 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. 22 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.

23. 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. 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. 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 Overlapping area Agenti Agentj Vehicle track

26. 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. 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. 28 Partial Global Planning (PGP) 4 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.

29. 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. 30 Partial Global Planning (PGP) 6 A DAI testbed – revisited. Overlapping area Agenti Agentj Vehicle track j i

31. 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. 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. 33 Multi-agent Planning 2 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

34. 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. 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. 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. 37 Multi-agent Planning 6 Sometime Plans can also become obsolete very quickly. i.e. Short life-span.

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

39. 39 Comparing Common Coordination Techniques Organisation Structures Meta-level Information Exchange Multi-agent Planning low less high more PredictabilityPredictability ReactivtyReactivty InfoExchangeInfoExchange

40. 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. 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 =>  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. 42 Social Norms and Laws 2 Example: Feature interaction in telecommunications Uses deontic logic (model obligations) Process incoming call Incoming call screening Incoming call answer Forward call Accept call Recall Forward #1 obliged forbidden obliged

43. 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. 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. 45 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. 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. 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. 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. 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. 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: 1.When to keep a commitment (retain) 2.When to revise a commitment (rectify) 3.When to remove a commitment (abandon)

51. 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. 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. 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. 54 Teamwork Definition American Heritage Dictionary –Cooperative effort by the members of a team to achieve a common goal.

55. 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. 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. 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 g2g3g1 ijk Agents i, j and k are a team and have a common goal G.

58. 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 G g2g3g1 ijk

59. 59 Joint Intentions (Jennings) Revisited Social Conventions Team members must be aware of the convention that govern their interactions. e.g. G g1 g2 AND AiAiAjAj G g1 g2 OR AiAi AjAj 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. 60 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 g2g3g1

61. 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. 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. 63 Let’s take a minute…… Discuss with your neighbour an example of teamwork.

64. 64 Next Lecture: Agent Communication Will be based on: ”Communication”, Chapter 8 in Wooldridge: ”Introduction to MultiAgent Systems”