Key Issues in Multi-Agent Coordination
Victor R. Lesser
Computer Science Department
University of Massachusetts, Amherst
Fifth Americas School on Agents and
Multiagent Systems
Harvard University
July 15, 2006
A Multi-Agent System (MAS):
Groups of Sophisticated AI systems that Work
Together
•Limited
Bandwidth
•Lack of Global
View
•Decentralized
Control
•Autonomous,
Asynchronous
Subsystems
•Need for
Cooperation
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Goals of Lecture
• Understand the Importance of
Cooperation and Coordination in MAS
– What is it
– When do you need it
– Key issues in designing coordination
mechanism
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Why Do Agents Want to
Work Together?
Subproblem Interdependencies
Engenders the Need for Cooperation/Coordination
• Subproblems are the same/overlapping, but different agents
have either alternative methods or data that can be used to
generate a solution.
• Subproblems are part of a larger problem in which a
solution to the larger problem requires that certain
constraints exist among the solutions to its subproblems.
• Not possible to decompose the problem into a set of
subproblems such that there is a perfect fit between the
location of information, expertise, processing, and
communication capabilities in the agent network and the
computational needs for effectively solving each
subproblem.
• Contention for resources or through relationships among
the subproblems.
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Implications of Subproblem
Interdependencies
 May be impossible to completely solve one subproblem
without first partially solving another subproblem
 Solving or partially solving one subproblem may simply
make it easier to solve another subproblem
 Knowing the solution to one subproblem may obviate the
need to solve another.
How an agent orders which subgoals to do and
when to communicate the (partial) results of
solving a subgoal can significantly affect
performance
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Where do
Subproblems/Agents in MAS
Come From
• Spatial, Functional or Temporal
distribution of
– information, expertise, resources, sensing
and effecting
• Separate authority (lines of control) over
resources
– organizational imperatives
• Layered systems’ architectures
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What is Coordination?
“Coordination is the process of managing
interdependencies between activities ” (on
different agents)
- Malone and Crowston, 1991
Coordination problems occur when:
• An agent has a choice in its actions within some task, and
the choice affects its and other agents’ performance
• The order in which actions are carried out affects
performance
• The time at which actions are carried out affects
performance
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Another Take on Defining
Coordination
• Deciding for each agent in the context of other
agent activities
– What activities it should do, when it should do
them and how it should do them (coordination)
– What it should communicate, when it should
communicate and to whom (cooperation)
• Domain information and Control (meta-level) information
• Communication and Scheduling of activities
intimately connected
• This is a highly complex problem and
computing optimal policies for agents in the
most general case is NEXP complete
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Many Approaches To Coordination
• Character of coordination problem
• Type of Information available about Static and
Dynamic Behavior of agents
– Cost of acquiring current state of other agent
• Importance of optimal solution
– Cost of computing coordination decisions
– Implications of generating non-optimal coordination
• Real-time requirements
– How long do you have to make a decision
There is no one best approach to Coordination
Complex Multi-attributed Optimization Problem
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Example MAS Application:
Distributed Sensor Network
• Small 2D Doppler
radar units (30’s)
– Scan one of
three 120
sectors at a
time
• Commodity
Processor
associated with
each radar
• Communicate
short messages
using one of 8
radio channels
• Triangulate
radars to do
tracking
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Coordination Issues
• Need for Dynamic Coordination/Distributed
Resource Allocation
– Multiple sensors need to collaborate on tasks
• View objects of interest from multiple angles with different
types of sensors
• Sensing time windows need to be closely aligned
– Environmental Dynamics
• Sensor configuration changes as target moves
– Potential for Resource Overloads
• Multiple target in overlapping sensor regions
• Limited Communication Channels
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Coordination Issues, cont.
• Soft Real-time Coordination
– Limited time window for sensing
– Must anticipate where target is moving in order to effectively
allocate sensor resources
– Time for coordination affects time for sensing
• Distribution: communication latency/limited bandwidth
precludes global knowledge/control
– distributed data fusion
• Scalability: need to be able to handle large numbers of
sensor nodes
• Robustness: local failures should not induce global
collapse
– Handle uncertain information, sensor/processor/communication
failures
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Coordination Design Issues
• What are the different roles/goals that agents
handle
– Are these static or dynamically assigned
• How are agents organized
– Hierarchical, peer-to-peer
– Who makes the decision about role assignment
– Does the organization change over time
• What type of protocol is used for information
fusion
– How is it decided to what, when and to whom to
communicate
– Does all information need to be transferred
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Coordination Design Issues, cont
• What type of protocol is used for agents to
coordinate their activity
– What type of coordination over resources and
relationships among activities is needed
– How many agents are involved in coordinating ( 2
or n)
– How centralized should these protocols be
• How to handle incomplete,missing,
inaccurate information for control and domain
problem-solving -- dealing with uncertainty
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How much Decentralization
• Degree of control/data centralization
– Optimality of decision based on amount of non-local
context exploited
• How important is optimality?
– Cost of acquiring non-local context
• End-to-End Delays
• Overloading of communication channels
– Computational Processing required to analyze larger
context
• Different Issues need different degrees of
partial centralization
– System Architecture may mix and match different
mechanisms on an issue by issue basis to achieve
an appropriate levels of control centralization
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Coordination Design Issues, cont
• Can the system learn how to be more efficient
over time
• What is the relationship between local agent
control and coordination among agents
• How should agents relate to each other
– Self-interested vs. cooperative
• How can the system as whole continue
functioning if sensors, processors or
communication channels fail ……
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Is it computationally
practical to build
optimal coordination
strategies?
Coordination Complexity Graph
( Shen et al)
NEXP-complete
NP-complete
NP-complete
NP-complete
P-complete
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NearlyDecomposable
MAS (Simon)
Why is Coordination
Computationally Feasible
• Limited
interaction
across agent
clusters
from
M. Wooldridge's An
Introduction to MultiAgent
Systems. Copyright 2002.
John Wiley & Sons, Ltd.
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Satisficing
Developed by March and Simon to explain how
complex organizations function when there is significant
environmental uncertainty
• Answers meeting less stringent criteria are often still useful
in face of unacceptable computational costs to produce
answers that always meet the more exacting criteria
• Coordination policies that optimize along all the
dimensions of resource utilization may be computationally
too expensive
- in the face of the wide range of uncertainties
Cooperation strategies that produce acceptable solutions
using a reasonable amount
of processing resources
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Is coordination very different
among Cooperative vs. SelfInterested Agents?
• Cooperative…
…agents work toward common goal
• Self-interested…
…agents work toward own goals but
require help from other agents to
complete them
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Cooperative vs. Self-Interested
Agents
• Does this lead to different approaches?
- cooperative agents may disagree because of conflicting local
perspectives
- cooperative agents may contribute to common goals by
following own local goals (skeptical agents)
- self-interested agents may be willing to share information if
there is a lot of uncertainty in their decisions
• Is the utility function for evaluating actions the only difference
between cooperative and self-interested agents?
- doing an optimal calculation with complete (global) and upto-date information may be impractical for either approach
- approximate calculations with partial and out-of-date
information blur the boundaries
Can mechanisms developed for one being used for the other?
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Semi-Cooperative Agents
• Emerging Idea from Open Agent Systems
• Self-interested Agents come together to
form agent coalitions/teams/organizations
– Similar to virtual enterprises
• Trust and Reputation Mechanisms cause
self-interested agents to carry through on
commitments made when they agree to join
– Can ignore concerns about cheating and truthtelling?
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Can we understand
coordination from a
more abstract
perspectives?
Coordinating Distributed Search
Agent1
Agent2
G10
G20
OR
AND
G11
G12 . . . . . . G1k
G1,2m
G2p
AND
...........
G2t
AND
OR
G11,1
G11,2
G1m,1
G2m,2
Data/
Resources
G2p,2
AND
OR
AND
G1m,1,1
G2p,1
G1m,1,2
G2p,1,3
(G2p,1,4)
G2p,2,2
dr2j+1 …………………………………………….
dr2
A distributed goal search tree involving Agent1 and Agent2. zThe dotted arrows indicate interdependencies
between goals and data/resources in different agents, solid arrows dependencies within an agent. The
superscripts associated with goals and data indicate the agent which contains them (Jennings, 1993 based
on Lesser, 1990).
dr11 …………………………………… dr1j
Example
Abstract
Coordination
Problem
Jointly shared
G Agent 1
Agent 2
G1,20
AND
G1,21
G12 … G1k-1
G1,2k . . .
OR
AND
G11,1
DATA/
RESOURCES
G 2n
G21,2
d1 . . . . . . .
G1,2k,1
rj
G2k,2
dj+1 . . . . . . rm. . . . . dz
• Should they simultaneously pursue
• Should one agent delay executing its subgoal
alternative ways of achieving some
until it gets the facilitating results of another
1,2
2
subgoal (e.g., G K, 1 and G k, 2 solving
subgoal being solved by the other agent (e.g.,
for G1,2k)
G2k, 2 waiting for the result from G11, 1).
A More Complex Example Coordination Problem
AGENT A
AGENT B
TA
TB
max
max
TA1
min
A1
TB1
TA2
min
sum
sum
A3
q(2.1)
d(60)
q(2.4)
d(40)
q(3.6)
d(60)
TB2
B1
B3
q(2)
d(30)
A2
A4
B2
B4
q(4.8)
d(60)
q(3.2)
d(20)
q(4.5)
d(30)
q(2.7)
d(70)
A1
B1
A2
B2
Deadline 160
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Possible Schedules
Agent A (dl=160)
A1->A2 (3.6 Exp-quality)
A1->A2 with A4
A1->A2 with A3
A3->A4 (2.4 Exp-quality)
A3->A4 with A1
A3->A4 with A2
Agent B (dl=160)
B1->B2 (quality?)
B1->B2 with B3
B1->B2 with B4
B3->B4
B3->B4 with B1
B3->B4 with B2
Why is quality unknown?
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Uncertainty in Coordination
AGENT A
TA1
TB
max
max
TA2
min
TB1
min
A1
B1
q(10% 0) (60% 2) (30% 4)
d(40)
A2
B2
q(10% 0) (30% 3) (60% 6)
d(30)
q(40% 2) (60% 4)
d(20)
A1
Leads to the
need for
communication
among agents
and
contingency
plans
sum
B3
q(10% 0) (80% 2) (10% 5)
d(60)
A4
q(20% 0) (80% 6)
d(60)
TB2
sum
A3
q(20% 0) (30% 2) (50% 6)
d(60)
AGENT B
TA
q(50% 1) (50% 3)
d(30)
B4
q(10% 0) (90% 3)
d(70)
A2
a)
B1
B2
1
A1
b)
B1
7
A2
2
A3
5
A4
6
1
3
B2
2
B3
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4
B3
Deadline 160
1) A1 success
2) A1 fail
3) A2 success
4) A2 fail
5) A3 success
6) B1 success
7) B1 fail
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More Thoughts on Example
• Not optimal Coordination
• Only use success/failure state
• Turns out it is always worthwhile to do B3
before doing B2
– Have time
– Handle cases where B1 (q=2) and B2 (q=0)
• Example shows clear trade-off between
amount of communication and expected
quality generated by plan
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DEC-MDP
1
a1
o1
a
2
2
world
o2
• Multiple cooperating agents with potentially different observation
of the global state
• Each agent’s actions may affect
– Global state
– the other agent’s observations (which can represent
communication)
– The global reward
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DEC-MDP
• n-agent DEC-MDP: S, A, P, R, ,O
–
–
–
–
–
–
–
S is a set of world states
A  A1   An is the set of joint actions
P  S  A  S   is the transition function
R  S  A  S   is the reward function
  1   n is the set of joint observations
O  S  A  S     is the observation function
Joint full observability
• A policy   1, , n where  i : i  Ai
• Objective: find a joint policy that maximizes the
expected long-term reward from a start state
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DEC-MDP Policy
• policy   1, , n where  i : i  Ai
X:
Y:
X’s action
X’s observation
Y’s action
Y’s observation
Each agent’s
observation sequence:
its distribution of
belief in the current
world state
Stage t
Each agent’s new
observation sequence
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TAEMS to DEC-MDP
AGENT A
TA1
TB
max
max
min
A1
TA2
TB1
min
A2
q(20% 0) (80% 6)
d(60)
TB2
sum
A3
q(20% 0) (30% 2) (50% 6)
d(60)
AGENT B
TA
sum
B1
q(10% 0) (60% 2) (30% 4)
d(40)
A4
q(40% 2) (60% 4)
d(20)
B3
q(10% 0) (80% 2) (10% 5)
d(60)
B2
q(50% 1) (50% 3)
d(30)
q(10% 0) (30% 3) (60% 6)
d(30)
B4
q(10% 0) (90% 3)
d(70)
A3,B2
S0
A1,B1
A1,B3
A3,B1
A1,B3
V. Lesser Fifth Americas School on Agents and Multiagent Systems
S1: q = 0, 0
oA: q = 0,
oB: q = 0
A3,B3
S2: q = 0, 2
…
S9: q = 6, 5
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DEC-MDP complexity
• Finite horizon DEC-MDP (DEC-POMDP) is
NEXP-Complete in number of states
“In the DEC-POMDP, each agent maintains the
belief about the current global state just as in the
POMPD case. Additionally, it also maintains the
belief about the beliefs of the other agents in the
system. This belief of beliefs gives the problem a
complexity of NEXP-complete (J. Shen)”
• Look for meaningful subclasses of DEC-MDP
with lower complexity
• Exploit structure in problems
– Local subproblem for each agent with limited
points of interaction
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How does coordination
relate to cooperation or
sharing of information
Coordination Example:
Distributed Vehicle Monitoring
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Constraints among Subproblems
in Vehicle Monitoring
Three types of interdependencies among solutions to
agents’ subproblems:
 solutions involving overlapping regions of interest
among agents must be consistent,
 “track” hypotheses that can extend into other agents’
areas must be consistent,
 agents must be able to find appropriate external
evidence when the hypotheses require evidence
which could be in other agents’ areas

ghost track source (explanation)

tracking a wide-area formation of vehicles.
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sensor
1
MAP
sensor
2
Region 1, 2, 3, 4
Algorithms
raw data
Database
sensor
4
sensor
3
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Incomplete Decomposition
sensor
1
Algorithms
Algorithms
Database 1
Database 2
+DB2,3,4
+DB1,3,4
MAP
sensor
2
MAP
Region 2
Region 1
Dependences
leads to
interaction
MAP
MAP
Region 4
sensor
4
Region 3
Algorithms
Algorithms
Database 4
+DB1,2,3
Database 3
+DB1,2,4
sensor
3
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Communication Coordination:
noise
..
missing
This is an example of a two-agent distributed aircraft monitoring scenario. The
left-hand figure is the acoustic input to the two agents and the right-hand figure
is the final interpretation of the agents. The data point symbols represent the
positions of groups of acoustic signals detected by the sensors. The signals
were generated and the subscripts indicate the agent receiving the data. Data
points include the position of the signal source and the frequency class of the
signal. The shading of each box indicates the loudness of the sounds being
sensed (the darker the shading the louder) and is an indication of the likelihood
of the sensory data being correct. Box 4’A, which only appears in the final
solution, is not directly supported by acoustic data (only high-level predictions)
and thus is not shaded. T1 is a vehicle track and G1 is a ghost track caused by
the environmental reflections of sounds from T1.
The Need to Share Results
Agents A and B must communicate in order to converge on the
correct solution and to produce reasonable levels of certainty in their
solutions.
Without any communication,
 Agent A would incorrectly interpret its input data (for times 1
through 7) as a ghost track.
 Agent A’s sensor has failed to detect any signals from track T1
at times 4 and 5 (i.e., at T1 points 4A and 5B in the final solution)
 the most credible interpretation of the data from agent A’s local
perspective involves combining the fragment of actual vehicle
data at times 1 through 3 with the ghost data at times 4 through
7 into a long ghost track.
 Agent B would be uncertain of its interpretations of its data (the
time 5 through 10 portion of track T1) because of the limited
number of points over which it is able to track the vehicle.
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Cooperative Approach
This example also shows that a complete answer map could not be created from
the agents’ independent solutions. There would have to be major
adjustments of some of the individual interpretations.
•
Agent A uses agent B’s portion of track T1 as predictive information,
allowing agent A to make assumptions about its sensor having missed signals
at times 4 and 5 that could complete track T1.
•
Agent A produces an acceptable interpretation for the remainder of its original
ghost track (times 4 through 7 data), based on communication with agent B to
confirm most of this data (times 5 through 7 in the overlapping region) as
ghost data and can provide a source (T1) for the G1 ghost track.
•
Agent B’s uncertainty over its interpretations of its data (the time 5 through 10
portion of track T1) because of the limited number of points over which it is
able to track the vehicle is decreased due to agent A’s ability to find a
continuation of the track in its area.
The cooperative adjustment process requires back and forth
communication between the agents rather than simply having
one agent’s “better” solutions override the others.
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Implications for Cooperative Problem Solving
How agents cooperate by sharing partial results is crucial
to effective system performance because the partial
solution to a subproblem may have important ramifications
for problem solving throughout the system.
The lack of effective cooperation (or what is commonly called
global coherence) can lead to significant degradation in system
performance due to:
 agents not generating and communicating, in a timely manner,
solutions to specific subproblems that provide key constraints for
further progress in overall problem solving;
 agents generating and communicating redundant results or results
that are no longer appropriate given current progress in system
problem solving;
 agents having no useful work to perform because of the
inappropriate distribution of load among them.
Why is Coordination Difficult?
An agent will have difficulty in choosing and
temporally ordering its actions because:
• it has an incomplete view of the structure of the
overall task of which its actions contribute to
• It has an incomplete view of the current
allocation of resources in the environment
• the task structure and environment is changing
dynamically
• the agent is uncertain about the outcomes of its
actions, actions of other agents and the
accuracy of its information
Bounded Rationality/Limited
Computation&Information
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Overall Goals of Cooperative
Agent Coordination
• coverage
– any given portion of the overall problem must be
included in the activities of at least one agent
• connectivity
– agents must interact in a manner which permits
the covering activities to be developed and
integrated into an overall solution
• capability
– coverage and connectivity must be achievable
within the communication and computation
resource limitations of the network
Optimizing Social Welfare
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Detailed Goals of
Coordination
• To increase the task completion rate through parallelism
– To increase the solution creation rate by forming subsolutions in
parallel
– To minimize the time that agents must wait for results form each other
by coordinating activity
• To increase the set or scope of achievable tasks by sharing
resources (physical devices, information, expertise, etc.)
– To improve the overall problem solving by permitting agents to
exchange predictive information and constraints
– To improve the use of computer resources by allowing agents to
exchange tasks
– To improve the use of individual agent expertise by allowing agents to
exchange tasks.
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Detailed Goals of
Coordination
• To increase the likelihood that a solution will be
found despite problem solving or resource failures
– To increase the confidence of a (sub)solution by having
agents verify each other’s results through rederivation
using their potentially different expertise
– To increase the probability that a solution will be found
despite agent failures by assigning important tasks to
multiple agents, possibly using different methods to
perform those tasks
– To quickly recognize failure and be able to reconfigure
problem solving and resources to find alternative means
to solve important subgoals
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Detailed Goals of
Coordination
• To decrease the interference between tasks
by avoiding harmful interactions.
– To increase the variety of solutions by allowing
agents to form local solutions without being overly
influenced by other agents — distraction
– To reduce the amount of unnecessary duplication
of effort by letting agents recognize and avoid
useless, redundant activities
– To reduce the communication resource usage by
being more selective about what messages are
exchanged
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Difficulties of Agent
Coordination
Uncertainty in Agent View
• incomplete view of the structure of the overall task of
which its actions contribute to
• incomplete view of the current allocation of
resources in the environment
• the task structure and environment is changing
dynamically
• uncertain about the outcomes of its actions, actions
of other agents and the accuracy of its information
Incompleteness, Inconsistency and Incorrect
Information
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Difficulties of Agent Coordination
(cont’d)
• Complex Utility Function —
balancing coherency, flexibility,
reliability, and cost of coordination
• Limited communication bandwidth
- hardware, software (packaging
and assimilation)
• Limited (bounded) rationality
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Cooperative Control
Uncertainty
Uncertainties of an agent’s view of the state of
problem solving in other agents
• which agents are working on interacting subproblems
(goals)
• how difficult are these subproblems
• when are they expected to be solved
• what progress has been made in solving them
• if they have been solved what is the character of the
solutions
Leads to uncertainties in decisions concerning the
communication of information among agents and the
choice of which goals to pursue locally
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Uncertainties in
Communication Decisions
To whom should an agent transmit its results and
goals?
What type of results or goals should it transmit?
When and under what conditions should it
communicate?
What protocols should it use?
What credence should it give to information received
from specific agents?
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Additional
Cooperative Control Uncertainties
• What type of solution to a goal would be most beneficial to
the problem solving of other agents and when should it be
generated?
• What is the appropriate balance between pursuing locally
generated activities versus responding to externally
received requests?
• Should different priorities be attached to the type of
request and the agent doing the requesting?
• Task allocation decisions for load-balancing, which are
based on the type of work agents will do in the near-term
and the character of the expected results
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Review of Coordination Issues
• How to ensure agents act coherently when making
decisions or taking actions associated with interrelated
problems
• How to guarantee that system-wide criteria (e.g.,
optimality of solution, hard real-time deadlines, etc.)
are met when the results of multiple agents need to be
integrated
• How to recognize workload imbalances and
appropriately redistribute activities and responsibilities
among agents
• How to enable individual agents to represent and
reason about actions, plans, and knowledge of other
agents in order to coordinate with them
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Summary of Key Ideas in
Coordination of Cooperative Agents
• Agent Flexibility in Open Environments
– Agents need to be able to adapt their local problem solving to the
available resources and goals of the system.
• Long-term learning needs to be an integral part of an agent
architecture
– Agents not restricted to solving one goal at a time but may flexibly
interleave their activities to solve multiple goals concurrently
– Error resolution/management needs to be integral part of agent
problem solving
• Satisficing control
– Less than optimal but still acceptable levels of coordination among
agents is traded off for a significant reduction in computational
costs to implement cooperative control.
– Emphasis on satisficing behavior subtly moves the focus from the
performance of individual agents to the properties and character of
the aggregate behavior of agents.
V. Lesser Fifth Americas School on Agents and Multiagent Systems
58
Key Ideas (continued)
• Predicting Performance of MAS systems is possible via
probabilistic analysis
– Requires detail model of the environment
• Interaction between local and non-local agent control
– For effective agent coordination local agent control must have a
certain level of sophistication in order to be able to understand
what it has done, what is currently doing and what it intends to
do
• Agent Roles and Responsibilities for large agent
societies
– organizing agents in terms of roles and responsibilities can
significantly decrease the computational burden of coordinating
their activities.
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V. Lesser Fifth Americas School on Agents and Multiagent Systems
Key Ideas (continued)
• Centrality of Commitment to coordinated behavior
– Both long- and short-term coordination can be viewed in
terms of commitments that have varying duration and
specificity.
• Layered Control
– Modulation—higher layers providing constraints
(policies) to lower levels that modulate (circumscribe)
their control decisions
– Bi-directional Interaction(negotiation) among Layers —
Though constraints flow down the layers, information
that flows in the other direction allows these constraints
to be modified in case they can’t be met or they lead to
inappropriate behavior
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V. Lesser Fifth Americas School on Agents and Multiagent Systems
Key Ideas (continued)
• Situation-specificity
– There is no one best approach to organizing and
controlling computational activities for all situations
when the computational and resource costs of this
control reasoning is taken into account.
• Quantitative View of Coordination
– Efficient and effective coordination must account for
the benefits and the costs of coordination in the
current situation.
– Coordination can be seen as a distributed
mechanism for approximating a global optimization
problem of task assignment
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V. Lesser Fifth Americas School on Agents and Multiagent Systems
Key Ideas (continued)
• Domain-independence —The aspects of a
domain that affect coordination can be
abstracted and represented in a domainindependent language.
– An agent’s goals and criteria for their successful
performance
– The performance characteristics and resource
requirements of the alternative methods it
possesses for accomplishing its goals,
– Qualitative and quantitative interdependencies
among its methods and those of other agents
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V. Lesser Fifth Americas School on Agents and Multiagent Systems
Key Ideas (continued)
• Self-Aware Control -- Representing and Reasoning
about Assumptions
– To the degree that the system can either re-derive or explicitly
represent the assumptions behind these control decisions
– The more the system can effectively detect and diagnose the
causes for inappropriate or unexpected agent behavior.
• Importance of Experimentation — we are still an
experimental science
– Don’t yet have good ways to predict performance
– Statistical analysis is important but don’t forget to look at the
details
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V. Lesser Fifth Americas School on Agents and Multiagent Systems
A Model for Computation in
the 21st Century
Network of cooperating, intelligent agents
(people/machines)
• Constructionist perspective
– Organization of heterogeneous agents
– High-level artificial language for cooperation
– Problem solving for effective cooperation will be
as or more sophisticated than the actual domain
problem solving
• Reasoning about goals, plans, intentions, and
knowledge of other agents
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V. Lesser Fifth Americas School on Agents and Multiagent Systems
A Model for Computation in
the 21st Century, p.2
• Operate in a “satisficing” mode
– Do the best they can within available resource
constraints
– Deal with uncertainty as an integral part of network
problem solving
– Resolving conflicting viewpoints/Negotiation
• Complex organizational relationships among
agents
– Scaling to 100’s to 1000’s of agents
– Dynamically formed organizations out of semicooperative agents
– Institutions to maintain viability of organization
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V. Lesser Fifth Americas School on Agents and Multiagent Systems
A Model for Computation in
the 21st Century, p.3
• Highly adaptive/ highly reliable
– Learning will be an important part of their structure (shortterm/long-term)
– Able to adapt their problem-solving structure to respond to
changing task/environmental situations
There is a lot of exciting research
to make this model a reality!
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V. Lesser Fifth Americas School on Agents and Multiagent Systems
Summary
• Coordination is a Difficult Problem
• Coordination and Cooperation are
intimately interconnected
• There are many different types of
situations involving coordination
– Different mechanisms for different
situations
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V. Lesser Fifth Americas School on Agents and Multiagent Systems