SIF8072 Distributed Artificial Intelligence and Intelligent Agents

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

Lecture Outline • Recap from last week – CDPS and CNET • Coordination techniques • Common coordination techniques • Coordination based on human teamwork • Teamwork

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.

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.

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

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.

Cooperative Distributed Problem Solving (CDPS) 1. Problem decomposition 2. Subproblem solution 3. Answer synthesis Ref: Smith & Davis, 1980

A1 A2 A3 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 1.1 Task 1.2 Task 1.3

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

…..Task Allocation

A1 A2 A3 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.

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.

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

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

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

Product Manager 2 Product Manager I Salesman Designer Engineer Salesman Designer Engineer Alternative Coordination Structures 1Product Hierarchy

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

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

Alternative Coordination Structures 4Decentralised Market Product Manager 3 Product Manager 1 Product Manager 2 Designers Salesmen Engineers

Comparison of Organization Structures

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.

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.

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.

Agenti Overlapping area Vehicle track Agentj 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

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.

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.

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.

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.

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

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.

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

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.

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.

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.

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

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

high high more Multi-agent Planning P r e d i c t a b i l i t y R e a c t i v t y I n f o E x c h a n g e Meta-level Information Exchange Organisation Structures low low less Comparing Common Coordination Techniques

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.

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

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.”

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

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

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.

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.

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

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)

SIF8072 Distributed Artificial Intelligence and Intelligent Agents