Slides from Sobah Abbas Peterson

Slides from Sobah Abbas Peterson
Multi-Agent Systems (Chapter 9) Adapted with permission from Adina Magda Florea Slides from Sobah Abbas Peterson

Benevolent vs.. self-interested agents
Benevolent: cooperative distributed systems. (CDPS) Simplifies the task enormously. Self-interested- potential for conflict Slides from Sobah Abbas Peterson

Distributed problem solving
Group coherence - agents want to work together - cooperative agents Competence - agents must find ways to work together - coordinate to cooperate Task and result sharing - an agent has many tasks to do and asks other agents to do some of its tasks; then it should integrate the results Distributed planning - the problem to be solved is to design and execute a plan in a distributed manner, by many agents Slides from Sobah Abbas Peterson 3

Distributed Problem Solving
Motivations: Speed up through parallelization Distribution of expertise Distribution of Data, features change Problem is inherently distributed Distribution of Results General Steps Task decomposition Task allocation Exchange sub problem solutions Task accomplishment Results Synthesis (make whole) Slides from Sobah Abbas Peterson

Task Decomposition Partitioning of a task into sub-tasks for possible allocation to another agent Goal is to make sub-tasks independent Minimize coordination (so communication costs don’t outweigh gain) Minimize shared data Minimize share resources Task decomposition is a hard problem and generally performed a priori by system designers. Slides from Sobah Abbas Peterson

Task Allocation Homogenous Systems Agents identical, allocation simple since each is equally qualified to work on sub-tasks Heterogeneous Systems Sub-task requirements - matched to agent skills Potentially difficult problem (perfect match problem) Slides from Sobah Abbas Peterson

Which kind of system to build?
Homogenous systems are simpler Only one kind of agent to build Don’t have to consider agent skills when distributing sub-tasks Homogenous systems considered unsuitable for complex problems Low overall utilization of skills and resources Slides from Sobah Abbas Peterson

Agent Roles in Task Allocation
Agents can assume two roles Servers: Agents capable of providing a service Clients: Agents requiring a service Agents can be both I.e. An agent may use the services of other agents to complete a service is to providing to another agent Task allocation systems must provide a way to match clients with servers Slides from Sobah Abbas Peterson

Centralized Allocation Systems
3rd party manages client-server matching Hierarchical Subordination Superior agents order subordinates to carry out task. Typically a static, pre-defined agent organization “Egalitarian” - all agents considered “equal” Requires special “broker” or “trader” agents to manage client requests and server bids Allows centralized allocation techniques Slides from Sobah Abbas Peterson

Egalitarian Allocation System
RejectC C RequestA RequestA A B AcceptD AcceptD D Client Trader Servers Slides from Sobah Abbas Peterson

Distributed Allocation Systems
Each agent individually attempts to obtain required services Acquaintance Network Direct Allocation Agents can only use the services of the agents it knows about Potentially serious scalability issues Delegated Allocation Agents can ask other agents to use their acquaintances to find an agent capable of providing a particular service Requires strongly connect acquaintance network Both methods require accurate knowledge of agent skills May use various “caching” strategies to maintain and age acquaintance information Slides from Sobah Abbas Peterson

Distributed Allocation Systems (cont)
Contract Net “Market Place” approach Clients issue description of tasks Servers reply with bids Client chooses the best bidder Server affirms its commitment Proven approach from other disciplines/simple Well suited for dynamic environments Concurrent and many-to-many nature of the protocol creates challenging race conditions Slides from Sobah Abbas Peterson

Task Allocation System Tradeoffs
Centralized Distributed Trader Acquaintance Contract Net Benefits Coherence Drawbacks Bottleneck Fault Intolerance Benefits No Bottleneck Fault tolerance Drawbacks Coherence Scalability Latency Benefits Proven/Simple Flexibility Drawbacks Message volume Temporal & Spatial Ignorance Slides from Sobah Abbas Peterson

Types of Tasks Independent Tasks are self-contained Can be performed in any order and concurrently Interdependent The solutions of some sub-tasks are required for the solution of other sub-tasks Coordination possible if dependencies known before Possible dependencies only become apparent at runtime A Results Sharing mechanism is needed to solve these dependencies Slides from Sobah Abbas Peterson

Motivations for Results Sharing
Confidence: Independent derivations affirm/challenge previous results leading to more confidence Completeness: Combination of partial results leads to a larger set of results Precision: Sharing of results allows for iterative refinement (agents come to see interface) Timeliness: Obvious performance benefits via parallel processing Slides from Sobah Abbas Peterson

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. Result sharing is used in blackboard systems. A1 A2 A3 Slides from Sobah Abbas Peterson

Result Sharing Benefits
Confidence (checking solutions) Completeness/precision: share local views Timeliness: may get results faster (even if agent could do it himself) Slides from Sobah Abbas Peterson

What about inconsistency?
Ignore it – but are you throwing away the true information (the part that doesn’t fit the expectation)? Resolve it through negotiation Degrade gracefully progress opportunistically (not in strict predetermined order) communicate high level results, not raw data inconsistency resolved as you go (not at end) no single solution route (if one is problematic, try another) Slides from Sobah Abbas Peterson

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. Example 1 – the door is the resource, which we both want to use. But only one can use it at a time. Reference: Cartoon taken from Klein, AAMAS2002. Slides from Sobah Abbas Peterson

Coordination Techniques
Organisational Structures Multi-agent Planning Norms and social laws Coordination Models based on human teamwork: Joint commitments (Jennings) Mutual Modelling Recap definition of DPS from lecture 1: considers how the task of solving a particular problem can be divided among a number of modules that cooperate in dividing and sharing knowledge about the problem and its evolving solution(s). Slides from Sobah Abbas Peterson

Organizational Structuring
Organizes agents into an organization May be based on how the task was decomposed Agents use knowledge of the organization to Determine with whom to communicate Prioritize tasks Agents only need to know about the local organizational structure (coherence) Choosing an organization structure can, itself, be a difficult problem! Slides from Sobah Abbas Peterson

Organizational Structuring
Geographically distributed “cells” Slides from Sobah Abbas Peterson

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. Organizational structures may be: Functional (based on skills) Spatial (based on physical location) Temporal (based on time relationship) This is a functional approach or process-oriented. Slides from Sobah Abbas Peterson

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 T.W. Malone. Modelling coordination in organization and markets. Management Science, 33(3): , October 1987. Slides from Sobah Abbas Peterson Reference: Malone 1987

Alternative Coordination Structures 1 Product Hierarchy
Designer Product Manager I Salesman Engineer Designer Product Manager 2 Salesman Engineer Several divisions for different product lines. Each division has a product manager. Failures inside one department doesn’t affect other products. Also a model for separate companies (or divisions of an enterprise) 1 message to assign task and one to notify. Slides from Sobah Abbas Peterson

Alternative Coordination Structures 2 Functional Hierarchy
Product Manager (several products) Engineers Engineering Manager Designers Design Manager Salesmen Sales Manager Similar agents are pooled together into functional departments. Each department has a functional manager. Purpose: reduces duplication of effort. Product manager for several products. 2 messages to assign task and 2 messages to notify about result. Failure of a task agent – causes a delay and task must be reallocated. Managers are critical. Slides from Sobah Abbas Peterson

Alternative Coordination Structures 3 Centralised Market
Product Manager 3 Product Manager 1 Product Manager 2 Engineers Engineering Manager Designers Design Manager Salesmen Sales Manager Functional Managers Functional managers are brokers. Brokers are in contact with possible ”sellers” and will chosse the best. Fewer connections and communication are required. 2 messages – 2 to assign and 2 to notify. Similar to functional model. Failure of one product manager does not affect others. Slides from Sobah Abbas Peterson

Alternative Coordination Structures 4 Decentralised Market
Product Manager 3 Product Manager 1 Product Manager 2 All product manages have access to all agents and the agent to perform the particular task is selected based on market mechanisms. All ”buyers” (product manager) are in contact with all ”sellers” (task agent). Product managers can chosse the best task agent. m task agents -> 2m+2 messages. If task agent fails, the task must be reallocated. Designers Engineers Salesmen Slides from Sobah Abbas Peterson

Comparison of Organization Structures – the Issues!
Production cost Coordination Vulnerability Product hierarchy H L H- Funtional M- H+ Centralised market M+ Decentralised H: high M: medium L: low Changes in cost w.r.t. Size structure: Product hierarchy: vulnerability cost increases Functional hierarchy: coordination cost increases Centralised market: Coordination cost increases Deentralised market: Coordination cost increases; this increase is greater than that for a centralised market. Slides from Sobah Abbas Peterson

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. Slides from Sobah Abbas Peterson

Partial Global Planning (PGP)
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 Main problem with the domain was to process information as rapidly as possible so that the system could come to conclusions about the paths of vehicles in time for them to be useful. To coordinate the activities of the agents, Durfee developed an approach known as PGP. Reference for DVMT: K. S. Decker, ”Distributed Artificial Intelligence Testbeds”. In: G. M. P. O'Hare, N. R. Jennings (eds). Foundations of Distributed Artificial Intelligence, John Wiley & Sons, 1996, pp Slides from Sobah Abbas Peterson

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. Slides from Sobah Abbas Peterson

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. Slides from Sobah Abbas Peterson

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. Slides from Sobah Abbas Peterson

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. Slides from Sobah Abbas Peterson

A DAI testbed – revisited. Agenti Overlapping area Vehicle track j i Agentj Slides from Sobah Abbas Peterson

Multi-agent Planning 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. Slides from Sobah Abbas Peterson

Multi-agent Planning 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: Centralized plan Distributed plan Big difference between them! Slides from Sobah Abbas Peterson

Multi-agent Planning 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. Drawing parallels to task and result sharing: Task sharing: The overall problem formulation can be thought of as decomposition and distribution and different agents, each acting on a subproblem. e.g. In the software industry, the gui developer can prepare a partial plan for designing and implementing the gui and the database developer can prepare a plan for the database. Result sharing: Each agent can, instead of waiting until the partial plan is completed, share the results of their work. Slides from Sobah Abbas Peterson

Multi-agent Planning 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. Slides from Sobah Abbas Peterson

2 Distributed planning What can be distributed: The process of devising a plan is distributed among agents Execution is distributed among agents Planning State representation and plan representation Search vs. planning representation of changes to the world state representation of and reasoning about the plan (steps/actions) Planning  Search Slides from Sobah Abbas Peterson 42

2.1 Centralized planning for distributed plans
Operators move(b,x,y) move b from x to y  movetotable(b,x) Precond: on(b,x)  clear(b)  clear(y) Precond: on(b,x)  clear(b) Postcond: on(b,y)  clear(x)  Postcond: ob(b,T)  clear(x)  on(b,x) on(b,x)  clear(y) work backward from each “on” goal on(B,A) S1: move(B,T,A) on(B,T) clear(B) clear(A) movetotable(A,B) move(A,B,y) S2: move(A,B,E) clear(A) clear(E) on(A,B) ………….. …………. on(E,T) S3: movetotable(E,F) I'm Tom Agent2 I'm Bill Agent1 C A E B F D Sfinal A B D C E F Sinit on(A,B) on(C,D) on(E,F) on(B,T) on(D,T) on(F,T) on(B,A) on(F,D) on(A,E) on(D,C) on(E,T) on(C,T) 1. Given a goal description, a set of operators, and an initial state description generate a partial order plan Slides from Sobah Abbas Peterson 43

S1: move(B,T,A) To satisfy the preconditions, we have: S2: move(A,B,E) S2 < S1, S3 < S4 S3:movetotable(E,F) S6 < S4, S6 < S5 S4: move(F,T,D) Also S5: move(D,T,C) S2 threat to S3  S3 < S2 S6: movetotable(C,D) S4 threat to S5  S5 < S4 Then the partial ordering is: S3 < S2 < S1 S6 < S5 < S4 S3 < S4 Any total ordering that satisfies this partial ordering is a good plan for Agent1 2. Decompose the plan into sub problems so as to minimize order relations across plans 3. Insert synchronization 4. Allocate sub plans to agents Slides from Sobah Abbas Peterson 44

What if we have 2 agents? DECOMP1 Subplan1 S3 < S2 < S1 Subplan2 S6 < S5 < S4 and S3 < S4 Agent1 S3 < send(clear(F)) < S2 < S1 Agent2 S6 < S5 < wait(clear(F)) < S4 Slides from Sobah Abbas Peterson

S3: movetotable(E,F) S2: move(A,B,E) S1: move(B,T,A) S6: movetotable(C,D) S5: move(D,T,C) S4: move(F,T,D) DECOMP2 Subplan1 S3 < S5 < S4 Subplan2 S6 < S2 < S1 and S3 < S2 and S6 < S5 Agent1 S3 < send(don't_care(E)) < wait(clear(D)) < S5 < S4 Agent2 S6 < wait(don't_care(E)) < wait(clear(D)) < S2 < S1 Obviously, DECOMP2 has more order relations among sub plans than DECOMP1 Therefore, we choose DECOMP1 S3 < send(clear(F)) < S2 < S1 S6 < S5 < wait(clear(F)) < S4 But then back to DECOMP2 < < 4. If failure to allocate sub plans then redo decomposition (2) If failure to allocate sub plans with any decomposition then redo generate plan (1) 5. Execute and monitor sub plans I know how to move only D, E, F I know how to move only A, B, C Slides from Sobah Abbas Peterson 46

2.2 Distributed planning for centralized plans generate separate plans, then merge parallel result sharing may involve negotiation Agent 1 - is specialized in doing movetotable(b,x) Agent 2 - is specialized in doing move(b,x,y) PAgent1 = { S3: movetotable(E,F) satisfies on(E,T) S6: movetotable(C,D) satisfies on(C,T) no ordering } PAgent 2 = { S1: move(B,T,A), S2: move(A,B,E) satisfies on(B,A)  on(A,E) S4: move(F,T,D), S5: move(D,T,C) satisfies on(F,D)  on(D,C) ordering S2 < S1 and S5 < S4 } Merge PAgent1 with PAgent2 by checking preconditions and threats S3 < S2, S6 < S5, S3 < S4, S2 < S1 and S5 < S4 one agent executes (as is centralized) Slides from Sobah Abbas Peterson 47

The problem is decomposed , given to specialize similar to task sharing may involve backtracking Agent 1 - knows only how to deal with 2-block stacks Agent 2 - knows only how to deal with 3-block stacks A B D C E F Si C A E B F D Sf C A E B F D Slides from Sobah Abbas Peterson 48

2.3 Distributed planning for distributed plans a) Plan merging How much effort on coordinating issues? Agents formulate local plans to satisfy their goals Local plans are exchanged Local plans are combined analyzing for positive and negative interaction Add messages and/or timing commitments to resolve negative plan interactions and to exploit positive plan interactions Interacting situations Positive interactions between plans redundant actions beneficial actions Negative interactions between plans harmful actions exclusive actions incompatible actions Slides from Sobah Abbas Peterson 49

movehigh(b,x,y) Precond: have_lifter  clear(b)  clear(y)  on(y,z)  z T Postcond: on(b,y)  clear(x)  on(b,x)   clear(y)  free_lifter pick_lifter Precond: free_lifter Postcond: have_lifter  free_lifter Agent1: { S1:move(B,T,A) < S2: pick_lifter < S3: movehigh(E,T,B) } Agent2: { R1:move(C,T,D) < R2: pick_lifter < R3: movehigh(F,T,C) } Negative interactions what type? if both select same lifter A B D C E F Si R1 S1 need_l S2 S3 Sf1 D A B E F C Sf free_l B A C R2 R3 Slides from Sobah Abbas Peterson 50

Positive interactions Give examples of positive interactions redundant beneficial Problems with the approach? b) Iterative plan formation build all feasible plans build partial order plans to facilitate plan merging build abstract plans to be iteratively refined Slides from Sobah Abbas Peterson 51

c) Hierarchical distributed planning Each agent stores plans on several levels of abstraction Use abstract plans (hides details) Abstract operator - a kind of macro-operator = sequence of applicable operators Write paper Read references Organize ideas Type content Edit text ….. Locate Computer Check for errors Edit figures Slides from Sobah Abbas Peterson 52

Hierarchical behavior-space search algorithm 1. Level  0 (current level of abstraction), Agent_List = {Agent1, …, AgentN} 2. for i=1,N do if Pi is compatible with {PJ}, j=1,N, ji then Agenti removes itself from Agent_List (no problems) 3. if Agent_list = { } then exit 4. Let N be the new number of agents in Agent_List 4.1 Determine conflicts between {Pi} 4.2 if conflicts to be resolved at a lower level then (a) Level  Level (b) go to step 2 Sort agents in Agent_List 5.2 for i=1,N-1, in current ordering do (a) make Agenti the current superior (b) send Pi to each AgentJ, j=i+1, N (c) for j=i+1, N do - AgentJ checks compatibility of PJ with Pi and replan - AgentJ checks compatibility with PK, k=1,i-1 and replan A kind of CSP: - backward checking - forward checking Ordering: - what heuristic? Add exit condition for no solution Slides from Sobah Abbas Peterson 53

2.4 Distributed planning and execution Real world: incomplete and incorrect information a) Contingency planning Conditional planning - constructing a conditional plan that accounts for each possible situation or contingency that could arrive A B C Start on(A,B)clear(C)clear(A) Checkarm(Ag1) armbroken(Ag1) Ask Ag2 to move(A,B,C) armbroken(Ag1) A C B move(A,B,C) Context: armbroken(Ag1) Negotiate with Ag2 for it to achieve move … Plan to achieve on(B,A) Slides from Sobah Abbas Peterson Finish on(B,A)on(A,C) 54

Multi-agent Planning 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. Slides from Sobah Abbas Peterson

Multi-agent Planning Sometime Plans can also become obsolete very quickly. i.e. Short life-span. Reference: Mark Klein, Tutorial on Coordination, AAMAS2002. Slides from Sobah Abbas Peterson

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

Comparing Common Coordination Techniques A Look at the Issues
Organisation Structures Multi-agent Planning low less high more P r e d i c t a b l y R v I n f o E x h g Slides from Sobah Abbas Peterson

Social Norms and Laws 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. Slides from Sobah Abbas Peterson

Social Norms and Laws Process incoming call Incoming call screening call answer Forward Accept Recall Forward #1 obliged forbidden Example: Feature interaction in telecommunications Uses deontic logic (model obligations) Reference: M. Barbuceanu, ”Agents that Work in Harmony by Knowing and fulfilling Their Obligations”, 1998, American Association for Artificial Intelligence. Slides from Sobah Abbas Peterson

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.” Each person has the intention of stopping themselves from getting wet. The actions of others do not affect an agent’s intented action. A contrasting situation, where a group of dancers converge to a common point when called by the choreographer – this is a cooperative action because they each have the aim of meeting at a central point. Slides from Sobah Abbas Peterson

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 Discuss individual goal vs. Team responsibility w.r.t. the example. Reference: Levesque Slides from Sobah Abbas Peterson

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 Reference: Wooldridge, ”Introduction to Multi-agent Systems”, chapter 4 – Practical Reasoning Agents. Slides from Sobah Abbas Peterson

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. Slides from Sobah Abbas Peterson

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. The joint intentions’s model was initially proposed by Cohen and Levesque, ref: Teamwork, Cohen, P., Levesque, H. Nous, Special Issue on Cognitive Science and AI, 25, 4, 1991, Jenning’w work is based on this. Slides from Sobah Abbas Peterson

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 Slides from Sobah Abbas Peterson

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) Slides from Sobah Abbas Peterson

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. Slides from Sobah Abbas Peterson

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. Slides from Sobah Abbas Peterson

Teamwork Definition American Heritage Dictionary Cooperative effort by the members of a team to achieve a common goal. Slides from Sobah Abbas Peterson

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 Slides from Sobah Abbas Peterson

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 Slides from Sobah Abbas Peterson

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 g3 g1 i j k Agents i, j and k are a team and have a common goal G. Slides from Sobah Abbas Peterson

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 It is important to note that there are other models of teamwork, e.g. Shared Plans Theory (Grosz, 1996) – agent’s intended action directed towards a group’s joint action. In this course we do not study Teamwork in details. References: Grosz, B. Collaborating Systems, AI Magazine, 17(2), 1996. Cohen, P. R. And Levesque, H. J., Teamwork, Nous, 25, 1991. G g2 g3 g1 i j k Slides from Sobah Abbas Peterson

Joint Intentions (Jennings) Revisited Social Conventions
Team members must be aware of the convention that govern their interactions. e.g. G g1 g2 AND Ai Aj Both Ai and Aj must fulfill their commitments to achieve G. Either Ai or Aj must fulfill their commitment. Relate these ideas to an example. G g1 g2 OR Ai Aj There is a need for all agents in a team to inform other members of the status of their commitments! Slides from Sobah Abbas Peterson

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 G g2 g3 g1 Slides from Sobah Abbas Peterson

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. Reference: G. Tidhar and A. Rao and E. Sonenberg, "Guided team selection", In Proceedings of the 2nd International Conference on Multi-agent Systems (ICMAS-96). Kyoto, Japan, 1996. Slides from Sobah Abbas Peterson

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