Multi-Agent Systems (Chapter 9) Adapted with permission from Adina Magda Florea adina@wpi
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Transcript of Multi-Agent Systems (Chapter 9) Adapted with permission from Adina Magda Florea adina@wpi
Slides from Sobah Abbas Peterson 1
Multi-Agent Systems(Chapter 9)
Adapted with permission from Adina Magda Florea
Slides from Sobah Abbas Peterson 2
Benevolent vs.. self-interested agents
• Benevolent: cooperative distributed systems. (CDPS) Simplifies the task enormously.
• Self-interested- potential for conflict
Slides from Sobah Abbas Peterson 33
Distributed problem solvingDistributed problem solving
• Group coherenceGroup coherence - agents want to work together - cooperative agents
• CompetenceCompetence - 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 4
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 5
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 6
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 7
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 8
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 9
Centralized Allocation Systems• Centralized
– 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 10
Egalitarian Allocation System
ServersTraderClient
A B
C
D
RequestA
RequestA
RejectC
AcceptD
AcceptD
Slides from Sobah Abbas Peterson 11
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 12
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 13
Task Allocation System Tradeoffs
• 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
Distributed
Acquaintance Contract Net
Centralized
Trader
Slides from Sobah Abbas Peterson 14
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 15
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 16
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
Slides from Sobah Abbas Peterson 17
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 18
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 19
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.
Slides from Sobah Abbas Peterson 20
Coordination Techniques
• Organisational Structures
• Multi-agent Planning
• Norms and social laws
• Coordination Models based on human teamwork:
– Joint commitments (Jennings)
– Mutual Modelling
Slides from Sobah Abbas Peterson 21
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 22
Organizational Structuring
Geographicallydistributed“cells”
Slides from Sobah Abbas Peterson 23
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)
Slides from Sobah Abbas Peterson 24
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
Slides from Sobah Abbas Peterson 25
Alternative Coordination Structures 1Product Hierarchy
Designer
Product Manager I
SalesmanEngineer Designer
Product Manager 2
SalesmanEngineer
Slides from Sobah Abbas Peterson 26
Product Manager (several products)
Alternative Coordination Structures 2Functional Hierarchy
Designers
DesignManager
Salesmen
SalesManager
Engineers
EngineeringManager
Slides from Sobah Abbas Peterson 27
Alternative Coordination Structures 3Centralised Market
Product Manager 2
Designers
DesignManager
Salesmen
SalesManager
Engineers
EngineeringManager
Product Manager 1 Product Manager 3
FunctionalManagers
Slides from Sobah Abbas Peterson 28
Alternative Coordination Structures 4Decentralised Market
Product Manager 2
Designers SalesmenEngineers
Product Manager 1 Product Manager 3
Slides from Sobah Abbas Peterson 29
Comparison of Organization Structures – the Issues!
Production
cost
Coordination
cost
Vulnerability
cost
Product
hierarchyH L H-
Funtional
hierarchyL M- H+
Centralised
marketL M+ H-
Decentralised
marketL H L
Slides from Sobah Abbas Peterson 30
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 31
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
Overlappingarea
Agenti
Agentj
Vehicletrack
Slides from Sobah Abbas Peterson 32
Partial Global Planning (PGP)
• 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 33
Partial Global Planning (PGP)
• 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 34
Partial Global Planning (PGP)
• 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.
Slides from Sobah Abbas Peterson 35
Partial Global Planning (PGP)
• 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 36
Partial Global Planning (PGP)
• A DAI testbed – revisited.
Overlappingarea
Agenti
Agentj
Vehicletrack
ji
Slides from Sobah Abbas Peterson 37
Coordination Techniques
• Organisational Structures
Multi-agent Planning
• Norms and social laws
• Coordination Models based on human teamwork:
– Joint commitments (Jennings)
– Mutual Modelling
Slides from Sobah Abbas Peterson 38
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 39
Multi-agent Planning
• 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. Centralized plan
2. Distributed plan
Big difference between them!
Slides from Sobah Abbas Peterson 40
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.
Slides from Sobah Abbas Peterson 41
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 42 42
2 Distributed planning2 Distributed planning• What can be distributed:
The process of devising a plan is distributed among agents
Execution is distributed among agents
PlanningPlanningState representation and plan representationSearch vs. planning
• representation of changes to the world state
• representation of and reasoning about the plan (steps/actions)
Planning Planning SearchSearch
Slides from Sobah Abbas Peterson 43
2.1 Centralized planning for distributed plans2.1 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)
43
A
B D
C E
F
Sinit
C
A
E
B F
D
Sfinal
I'm BillAgent1
I'm TomAgent2
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)
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)
1. Given a goal description,a set of operators,and an initial state descriptiongenerate a partial order plan
work backward from each “on” goal
Slides from Sobah Abbas Peterson 44
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
44
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 45
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 46
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
46
< <
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 howto move only
D, E, F
I know howto move only
A, B, C
Slides from Sobah Abbas Peterson 47
2.2 Distributed planning for centralized plans2.2 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)
47
Slides from Sobah Abbas Peterson 48
• 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
48
C
A
E
B F
DSf
C
A
E B F
D
A
B D
C E
F
Si
Slides from Sobah Abbas Peterson 49
2.3 Distributed planning for distributed plans2.3 Distributed planning for distributed plansa) Plan merginga) 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
49
Slides from Sobah Abbas Peterson 50
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) }
50
A B D CEF
Si
D
A
B
E F
CSf
S1
S2
S3
R1
R3
R2
need_l
free_l
B
A
B
CSf1
Negative interactionswhat type?
if both select same lifter
Slides from Sobah Abbas Peterson 51
Give examples of positive interactions
• redundant
• beneficial
Problems with the approach?
51
Positive interactions
b) Iterative plan formationb) 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 5252
c) Hierarchical distributed planningc) 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
Readreferences
Organizeideas
Typecontent
Locate Computer
Edittext
Editfigures
Checkfor errors
…..
Slides from Sobah Abbas Peterson 5353
Hierarchical behavior-space search algorithm1. 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, ji
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 + 1 (b) go to step 2
5. 5.1 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 conditionfor no solution
Slides from Sobah Abbas Peterson 54
2.4 Distributed planning and execution2.4 Distributed planning and executionReal world: incomplete and incorrect information
a) Contingency planninga) Contingency planning• Conditional planning - constructing a conditional plan that
accounts for each possible situation or contingency that could arrive
54
move(A,B,C)
Start
Ask Ag2 to move(A,B,C)
Checkarm(Ag1)
Finish
Negotiate with Ag2for it to achieve move
Context:armbroken(Ag1)
armbroken(Ag1) armbroken(Ag1)
on(B,A)on(A,C)
on(A,B)clear(C)clear(A)A
B C
A
C
B
… Plan to achieve on(B,A)
Slides from Sobah Abbas Peterson 55
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 56
Multi-agent Planning
• Sometime Plans can also become obsolete very quickly.
i.e. Short life-span.
Slides from Sobah Abbas Peterson 57
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 58
Comparing Common Coordination Techniques
A Look at the Issues
Organisation
Structures
Multi-agent
Planning
low low less
high high more
Predictability
Reactivty
Info
Exchange
Slides from Sobah Abbas Peterson 59
Coordination Techniques
• Organisational Structures
• Multi-agent Planning
Norms and social laws
• Coordination Models based on human teamwork:
– Joint commitments (Jennings)
– Mutual Modelling
Slides from Sobah Abbas Peterson 60
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 61
Social Norms and Laws
• 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
Slides from Sobah Abbas Peterson 62
Coordination Techniques
• Organisational Structures
• Multi-agent Planning
• Norms and social laws
Coordination Models based on human teamwork:
– Joint commitments (Jennings)
– Mutual Modelling
Slides from Sobah Abbas Peterson 63
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.”
Slides from Sobah Abbas Peterson 64
• 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
Slides from Sobah Abbas Peterson 65
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
Slides from Sobah Abbas Peterson 66
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 67
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.
Slides from Sobah Abbas Peterson 68
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 69
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)
Slides from Sobah Abbas Peterson 70
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 71
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 72
Teamwork Definition
• American Heritage Dictionary
– Cooperative effort by the members of a
team to achieve a common goal.
Slides from Sobah Abbas Peterson 73
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 74
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 75
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.
Slides from Sobah Abbas Peterson 76
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
Slides from Sobah Abbas Peterson 77
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!
Slides from Sobah Abbas Peterson 78
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
Slides from Sobah Abbas Peterson 79
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.