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Challenging Problems and New Directions in Automated Negotiation. Ben Kwang-Mong Sim Editor, IEEE Transactions on Systems, Man & Cybernetics Guest-Editor-in-Chief, IEEE Systems Journal, Official Journal of The IEEE Systems Council (formed by 15 IEEE Societies)

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challenging problems and new directions in automated negotiation

Challenging Problems and New Directions in Automated Negotiation

Ben Kwang-Mong Sim

Editor, IEEE Transactions on Systems, Man & Cybernetics

Guest-Editor-in-Chief, IEEE Systems Journal, Official Journal of

The IEEE Systems Council (formed by 15 IEEE Societies)

Director, Multiagent Systems Lab.

http://infcom.gist.ac.kr/~kmsim/MAS

what is negotiation
What is Negotiation?
  • a process by which a group of agents communicate with one another to try and come to a mutually acceptable agreement on some matter
  • a form of decision-making with two or more actively involved agents who cannot make decisions independently (or achieve their goals unilaterally), and therefore must make concessions to achieve a compromise
outline
Outline
  • One-to-one (bilateral)
    • Complete Information - Optimal strategy
    • Incomplete Information - Learning (GA + BL)
  • Many-to-many (multilateral)
    • Market-driven Negotiation
    • Relaxed-criteria Negotiation
  • From e-commerce negotiation to Grid commerce negotiation
  • Complex Negotiation
    • Concurrent negotiation in multiple markets
    • Establishment and decommitment of contracts
    • Coordination
    • Grid resource co-allocation
the problem of bilateral negotiation

Buyer

Agent

Seller

Agent

The Problem of Bilateral Negotiation

0S, S: S’s deadline

IPS : S’s initial price

RPS : S’s reserve price

0B, B: B’s deadline

IPB : B’s initial price

RPB : B’s reserve price

price

price

0<lB<1

lB=1

lB>1

t

t

B

S

bilateral negotiation
Bilateral Negotiation
  • Negotiation with complete information

[Sim et al. 2007]

  • Negotiation with incomplete information

[Sim et al. 2007]

    • when one agent has an estimate of its opponent’s RP& deadline through Bayesian Learning (BL) and uses GA to search for an appropriate proposal taking into consideration its opponent’s estimated RP
    • when both agents have an estimate of each other’s RP & deadline through BL and use GA to search for an appropriate proposal taking into consideration an estimation of their opponent’s RP
    • Both agents do not have an estimate of each other’s RP
  • Reference

[Sim et al. 2007] K. M. Sim, Y. Guo and B. Shi. Agents that Negotiate Optimally and Rapidly. Proceedings of the IEEE Congress on Evolutionary Computation, 2007, Singapore, pp. 1007-1014.

bilateral negotiation with complete information
Bilateral Negotiation with Complete Information
  • Theorem 1.[Sim et al. 2007]Agent B achieves maximal utility when it adopts the strategy
  • Theorem 2. [Sim et al. 2007]Agent S obtains the maximal utility when it adopts the strategy
bilateral negotiation with complete information7
Bilateral Negotiation with Complete Information

S’s concession making:

S will propose RPS at S:

price

minimal possible

agreement-price

for B = RPs

Optimal strategy for B satisfies

t

S

B

B’s concession making:

bilateral negotiation with complete information9
Bilateral Negotiation with Complete Information
  • Theorem 1. [Sim et al. 2007]Agent B achieves maximal utility when it adopts the strategy
  • Theorem 2. [Sim et al. 2007]Agent S obtains the maximal utility when it adopts the strategy
bilateral negotiation with complete information10
Bilateral Negotiation with Complete Information

Non-optimal strategy

price

Agreement price >RPs

t

S

B

bilateral negotiation with complete information11
Bilateral Negotiation with Complete Information

Non-optimal strategy

price

No agreement

t

S

B

bilateral negotiation with incomplete information
Bilateral Negotiation with incomplete information
  • BLGAN containing two procedures [Sim et. Al 2007] :
    • BL-Procedure
    • GA-Procedure
  • BLGAN Overview :

In each negotiation round, after receiving the opponent’s proposal, the agent will decide whether to accept the proposal.

    • If it agrees, a successful deal is made.
    • Else, it generates the next proposal as follows:

(1) In BL-Procedure, a Bayesian updating method is adopted to learnopponent\'s RP & a procedure is used to estimate opponent’s deadline;

(2) Use the estimated opponent\'s RP and deadline, compute a new 

(3) Use  to generate possible proposal Probl;

(4) Compensate for error in estimating opponent\'s RP using a genetic algorithm (GA) to search for a better proposal within a dynamic search space confined to an area around Probl .

(5) A possibly better proposal is revised and sent to the opponent.

multi lateral negotiation
Multi-lateral Negotiation

e-market

Buyer

Agent

Seller

Seller

Agent

Buyer

Contract

Seller

Agent

Buyer

Agent

Seller

Buyer

Seller

Agent

Buyer

Agent

Seller

Buyer

slide16

current

Narrowing differences

kt

next

kt+1

D t

How much to concede?

Market-Driven Negotiation

Making adjustable amountsof concession

Time, Opportunity, Competition

slide17

The Role of Time

  • Bilateral negotiation - only time is considered.
  • kt+1 = T(t,, )kt

T(t,, ) = 1-[t/]

    • t : current round, : deadline,
    • [0, ] : stategy

Attitudes toward time

“Sit-and-wait”

k

l=

Conservative

Difference

l>1

l=1

k0

k

Conciliatory

k`

0<l<1

t

Linear

t

t`

t0

Negotiation round

slide18

k1

Outside Options, Conflict & Differences

Probability of

reaching consensus

v BS1

v S1B

B

S1

v S2B

v BS2

k2

S2

B

v BSn

v SnB

kn

Sn

B

kt+1 = O(n, v BSi, {v SiB})kt

O(n, v BSi, {v SiB})1, concede less to narrow difference

O(n, v BSi, {v SiB})0, concede more to narrow difference

rivalry competition

chance of not generating

the highest utility for its

trading parties

nTrading

parties

m-1 competitors

Each trading

party i

m-1 competitors

Rivalry & Competition

Rivalry inherent in many-to-many negotiation

Utility maximizing – most likely to reach agreement, if proposal ranked highest

Probabilityof being ranked highest by some trading party:

Buyer-seller ratio

  • kt+1 = C(m,n)kt
  • C(m,n)1, less competition  concede less
  • C(m,n)0, more competition  concede more

chance of not generating the highest utility for its trading party

adjustable amount of concession

kt

Adjustable Amount of Concession

current

Narrowing differences

Difference

next

kt+1

D t

time

kt+1= f[O(n,v BSi,{v SiB}),C(m,n), T(t,, )] kt

empirical results
Empirical Results

Conservative

Conciliatory

Linear

relaxed criteria negotiation
Relaxed-Criteria Negotiation
  • Conventional negotiation: a process by which a group of agents communicate with one another to try and come to a mutually acceptable agreement on some matter
  • Relaxed-criteria negotiation [Sim 2004]:a process by which a group of agents communicate with one another to try and come to a roughly acceptable agreement on some matter

Reference

[Sim 2004] K. M. Sim and S.Y. Wang. Flexible Negotiation Agent with Relaxed Decision Rules. IEEE Transactionson Systems, Man and Cybernetics, Part B, Vol. 34, No. 3., pp. 1602- 1608, Jun., 2004.

rubinstein s alternating offers protocol
Rubinstein’s Alternating Offers Protocol
  • Agents negotiate by making proposals in alternate rounds
  • If no agreement is reached, negotiation proceeds to another round.
  • Negotiation between two agents terminates:
    • (i) when an agreement is reached or
    • (ii) with a conflict when one of the two agents’ deadline is reached.
  • An agreement is reached when one agent proposes a deal that matches (or exceeds) what another agent asks for.
rubinstein s alternating offers protocol24
Rubinstein’s Alternating Offers Protocol

agreement

Counter-propose

accept

Start

negotiation

Propose

Counter-propose

deadline

reached

deadline

reached

accept

conflict

agreement

conflict

sim s relaxed criteria protocol
Sim’s Relaxed-Criteria Protocol
  • Agents negotiate by making proposals in alternate rounds
  • If no agreement is reached, negotiation proceeds to another round.
  • Negotiation between two agents terminates:
    • (i) when an agreement is reached or
    • (ii) with a conflict when one of the two agents’ deadline is reached.
  • Reaching Agreements:
    • R1: An agreement is reached when one agent proposes a deal that matches (or exceeds) what another agent asks for.
    • R2: An agreement is reached if the offer is sufficiently close (albeit, it does not totally match the agent’s bargaining terms).
relaxing criteria
Relaxing Criteria
  • How close is acceptable?
    • Remaining time (deadline fast approaching?)
    • Degree of competition
    • Relative distances between the proposal of an agent and all the proposals of its opponents

?

?

fuzzy decision controller fdc
Fuzzy Decision Controller (FDC)
  • Vague concepts :
    • fast approaching deadlines
    • strong competition
    • proposals are prettyclose
    • very urgent to acquire product

FDC

η

: degree of competition

: time pressure (remaining time)

: Relative distances between the proposal of

an agent and all the proposals of its opponents

slide29

A high value of indicates that an agent faces more competition

A high value of indicates that an agent’s deadline is fast approaching

A high value of indicates that the best proposal from its opponent is very close to

an agent’s proposal relative to all other proposals from all other opponents.

grid commercialization utility computing negotiation
Grid Commercialization, Utility Computing &Negotiation
  • Utility computing (http://www.sun.com/service/sungrid/index.jsp)
    • a business model whereby computer resources are provided on an on-demand and pay-per-use basis
    • seeks to maximize efficient use of computing resources and minimize user costs
  • Grid (http://www.gridcomputing.com/gridfaq.html)
    • cyberinfrastructure for coupling & pooling distributed resources, which can be used for realizing utility computing.
  • Negotiation (our work)
    • establishment & management of contracts
    • allocation of resources to meet competing demands from multiple consumers
from relaxed criteria e negotiation to relaxed criteria g negotiation
From Relaxed-criteria e-Negotiation to Relaxed-criteria G-negotiation
  • Applying Sim’s Relaxed-Criteria Negotiation to Grid Commerce
  • In a Grid market, consumers (applications) & resource providers negotiate to establish contracts for resource utilization.
  • Relaxed-Criteria Negotiation enhances success rates in acquiring resources & perhaps acquire resources more rapidly
relaxation criteria

?

?

Relaxation Criteria
  • How close is acceptable?
    • amount of relaxation (η)
  • Consumers
    • Recent statistics in failing/succeeding in acquiring resources (fst)
    • Demand for computing resources (dft)
  • Providers
    • Amount of resources currently being used (ult)
    • Recent resource requests from consumers (rft)
relaxation criteria34
Relaxation Criteria
  • Vague concepts :
    • high demand for computing resources
    • large amount of resources currently being used
    • proposals are pretty close

fst

dft

FDC-C

η

ult

rft

FDC-P

η

slide35

If I was quite successful in acquiring resources and I have very low resource demand

Then I would agree only if the difference is very small

slide36

If much of my resources are being occupied and I receive a lot of requests

Then I would be very unlikely to relax my bargaining terms

complex negotiation in multiple markets
Complex Negotiation in multiple markets

market 3

market 1

Buyer-1

Seller-3-1

Seller-1-1

Seller-3-2

Seller-1-2

Buyer-2

Seller-3-k

Seller-1-i

Seller-2-1

market 2

Seller-2-2

Buyer-3

Buyer-4

Seller-2-j

grid resource co allocation
Grid Resource Co-allocation

Grid Resource Co-allocation problem: to allocate to an application multiple resources belonging to possibly different administrative domains simultaneously before a deadline.

u se concurrent negotiation for grid resource co allocation
Use concurrent negotiation for Grid resource co-allocation?
  • Negotiation
    • obtain commitments or contracts from a resource owner to provide a service/resource
    • a means for different parties to resolve differences and conflicting goals
    • players in Grid marketplace to optimize their return-on-investment/cost
  • Concurrent Negotiation
    • Multiple negotiations to acquire multiple resources
    • Coordination is needed in Resource Co-allocation
concurrent g negotiation mechanism
Concurrent G-Negotiation Mechanism

Contracting

Coordination

concurrent g negotiation mechanism commitment managers cm
Concurrent G-Negotiation MechanismCommitment Managers(CM)
  • Each one-to-many negotiation has a commitment managerCMi
  • Each CMi
    • manages multiple concurrent bi-lateral negotiation threads
    • manages both commitment and de-commitment of (intermediate) contracts for resources
  • de-commitment
    • if a consumer cannot acquire all its required resources before deadline, it can release those resources acquired, so that providers can re-assign them to other consumers
    • allows an agent to reach an intermediate deal & continue to search for a better deal

one-to-manynegotiation for

Resource R1

concurrent g negotiation mechanism coordinator module
Concurrent G-Negotiation MechanismCoordinator Module
  • Each CMisends the predictedchange in utility in next negotiation round for resource Ri to the coordinator.
  • Using the information supplied by each CMi, coordinator decides when to terminate the entire concurrent negotiation process.

change in utility

in next round

contracting algorithm
Contracting Algorithm
  • Step 1: Estimate reneging probability
  • Step 2: Compute expected utility of provider’s proposal.
  • Step 3: Determine if provider’s proposal is acceptable taking into account penalty payment
  • Step 4: Request for contract.
  • Step 5: If receive confirmation then accept contract else revise proposal by making concession.
commitment management strategy step 1 reneging probability general idea
Commitment Management StrategyStep 1: Reneging Probability General Idea

Reneging prob. of a provider

associated with its price proposal

relative to others

Resource

Provider 1

Commitment

Manager i

Resource

Provider 2

Resource

Provider ni

  • the price proposals of providers 1, 2, …,ni
step 1 reneging probability
Step 1: Reneging Probability
  • Case 1: Very high prob. that resource provider reneges from deal
  • From consumer’s perspective, if proposal of Resource Provider j is too far below the average
    • can be easily chosen by other consumers.
  • Case 2: When distance of resource provider j’s proposal is below average, but not too far below average (e.g., WITHIN one standard deviation), it is believed that provider jis not likely to renege on deal
    • if provider j reneges, it needs to pay penalty
    • since it is close to the average, it is unlikely to obtain a better utility by breaking a deal

Case 3: Resource provider j’s proposal is above average

    • relatively good deal unlikely to renege
commitment management strategies step 2 expected utility
Commitment Management StrategiesStep 2: Expected Utility

Consumer’s expected utility from proposal of Resource Provider jat current round t :

Does not renege

Provider reneges

  • where is the consumer’s utility function for resource i.
  • utility function
    • preference ordering for negotiation outcome
    • higher number =>outcome more preferred
commitment management strategies step 3 acceptable proposals
Commitment Management Strategies Step 3: Acceptable Proposals

A provider’s proposal is acceptable to the consumer at current round t if it generates an expected utility that is equal to or higher than the utility generated from the consumer’s counter-proposal

Case 1: No previous commitment:

commitment management strategies step 3 acceptable proposals51
Commitment Management Strategies Step 3: Acceptable Proposals
  • the expected utility of thenew proposal must be greater thanprevious intermediate deal with provider k, after having paid penalty

Case 2: There is previous commitment with provider k at tik:

commitment management strategies step 4 request for contracts
Commitment Management Strategies Step 4: Request for contracts
  • If there are proposals that are acceptable, then the consumer will first send request for contracts to all corresponding resource provider agents, then wait for the confirmation of contracts from the resource provider agents.
commitment management strategies step 5 confirmations of contracts
Commitment Management Strategies Step 5: Confirmations of contracts
  • If the consumer receives one or more confirmations of contracts, it will accept the deal that generates the highest expected utility
    • if the consumer has already reached an intermediate deal with another provider, it will first renege on the deal before it accepts the new proposal.
    • send a confirmation of acceptance to the corresponding resource provider
  • Otherwise, consumer revises its counter-proposal using its time-dependent concession making function (Linear, Conciliatory and Conservative) and proceeds to the next round.
3 classes of commitment management strategies
3 Classes of Commitment Management Strategies
  • The concession-making strategy used by the consumer to generate its counter-proposal can affect results of negotiation
  • 3 classes of commitment management strategies (CMS) can be specified:
      • Linear-CMS
      • Conciliatory-CMS
      • Conservative-CMS
    • correspond respectively to Linear, Conciliatory and Conservative time-dependent concession making functions
n resource markets

Provider-1

Provider-1

Provider-1

Resource

Market 1

Provider-2

Provider-2

Provider-2

CM2

CM1

CMn

Provider-i

Provider-j

Provider-k

Resource

Market 2

Coordinator

Resource

Market n

N-resource markets
market types consumer s perspective
Market types (consumer’s perspective)

Ri-favorable market:more providers supplying Ri & fewer consumers competing for Ri.

Ri-unfavorable market:fewer providers supplying Ri & more consumers competing for Ri.

Ri-balanced market:almost equal No. of providers & consumers.

n-resourse market: a n-tuple <T1,...,Tn>, where each Ti is either a Ri-favorable market, a Ri-unfavorable market or a Ri-balanced market

fuzzy decision making approach
Fuzzy decision making approach
  • Step1: Calculate (function of the current concession of providers relative to previous concessions).
  • Step 2: Determine Bargaining Position (fuzzification).
  • Step 3: Determine (de-fuzzification).
coordination strategy
Coordination Strategy
  • Coordination needed for Concurrent Negotiation
    • failure of one negotiation => failure of the co-allocation
    • most important to ensure success of all concurrent negotiations
  • At each round:
    • Step 1: Predict Changes in Utility
    • Step 2: Decide whether to terminate or proceed
c oordination strategy step 1 predict change in utility
Coordination StrategyStep 1: Predict Change in Utility

Predicted change in utility:

Average utility in the

acceptable list at

current round t

Predicted Utility

with Provider j

at round

(using linear

regression)

Average predicted utility

at round

c oordination strategy step 2 proceed or terminate
Coordination StrategyStep 2: Proceed or Terminate

When ALL predicted changes in utility are calculated, coordinator computes total change:

where is weight of resource of consumer.

  • Ut<0
    • coordinator informs commitment managers that have not reached intermediate deal to accept the best proposal from its acceptable list
    • terminate the entire negotiation
  • Ut0
    • move to next round.
slide61

Terminate

May lose utility in coming rounds

Proceed

Utility-oriented Coordination Strategy

details on coordination for concurrent negotiation
Details on Coordination for Concurrent negotiation
  • Please come to the talk this afternoon:
  • A Regression-based Coordination for ConcurrentNegotiation

by Benyun Shi and Kwang Mong Sim

coordination in concurrent negotiation
Coordination in Concurrent Negotiation

Concurrent

one-to-one negotiation

  • I. Rahwan
    • Strategies for coordinating multiple concurrent one-to-one negotiations without decommitments
  • K. M. Sim & B. Shi
    • Strategies for coordinating multiple concurrent one-to-many negotiations with decommitments

coordinator

coordinator

Concurrent

one-to-many negotiation

slide64

one-to-one negotiation

  • Conventional Negotiation
    • contract established => both agents are bound to it.
  • T. Sandholm & V. Lesser Decommitments in a one-to-one negotiation
  • Nguyen & M. Jennings
    • Decommitments in multiple concurrent one-to-one negotiations
  • K. M. Sim & B. Shi
    • Decommitments in multiple concurrent one-to-many negotiations

one-to-one negotiation

decommitment

Concurrent one-to-one negotiation

decommitment

coordinator

coordinator

Concurrent one-to-many negotiation

decommitment

in grid resource management
In Grid Resource Management

propose

  • Agreement-based Grid Resource Management (K. Czajkowski, I. Foster & C. Kesselman)
    • Agreement Protocols:
      • notions for describing terms of an agreement
      • protocols for negotiating agreement terms (what negotiation patterns should be used for M2M negotiation?)
        • One shot: Propose/accept
        • Prolong: Propose/counterpropose*/accept
        • One shot reversible (contract can be broken)
        • Prolong reversible (contract can be broken)
      • protocols for establishing, monitoring and terminating an agreement
  • K. M. Sim’s concurrent negotiation (this work)
    • Prolong reversible (most complex)
  • K. M. Sim’s other works on market-driven negotiation & relaxed-criteria negotiation
    • Prolong

Outcome 1

accept

propose

Outcome 2

reject

propose

reject

Counter propose

reject

Counter propose

reject

Counter propose

accept/reject

summary and conclusion
Summary and Conclusion
  • Relaxed Criteria negotiation
    • enhance success rates and negotiation speed
  • Concurrent negotiation allows
    • Parallel establishments of multiple contracts
    • Parallel acquisition of multiple resources
    • Simultaneous negotiations in multiple markets
    • Flexibility in adopting different strategies against different opponents in the same market
  • Concurrent negotiation (allowing decommitments) & Relaxed-criteria negotiation are appropriate mechanisms for applications requiring complex negotiation for trading and exchange of resources
    • Exemplified by Sim & Shi’s Concurrent Negotiation Mechanism for Grid Resource Co-allocation & Sim & Ng’s Relaxed-criteria Negotiation for Grid Commerce
topics for phd dissertations
Topics for PhD Dissertations
  • Relaxed-criteria Negotiation as a pricing mechanism for Web Services
  • Relaxed-criteria Multi-attribute Negotiation
  • Relaxed-criteria Negotiation for Grid Resource co-Allocation
  • Concurrent automated negotiation for parallel procurement in supply chain management

Come discuss with me for other topics!!!

contact information
Contact Information
  • Ben K. M. Sim

Email: [email protected]

  • More information & my papers can be downloaded from:

http://www.comp.hkbu.edu.hk/~sim

http://www.iis.sinica.ed.tw/~kmsim

  • We welcome research collaborations!!!
some references
Some References
  • K. M. Sim. G-Commerce, Market-driven G-Negotiation Agents and Grid Resource Management. IEEE Transactions on Systems, Man and Cybernetics, Part B, Vol. 36, No. 6, December 2006, pp 1381-1394.
  • K. M. Sim. A Survey of Bargaining Models for Grid Resource Allocation. ACM SIGECOM: E-commerce Exchanges, Vol. 5, No. 5, January, 2006, pp. 22-32.
  • K. M. Sim. Equilibria, Prudent Compromises, and the "Waiting Game". IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol. 35, No. 4, Aug. 2005, pp. 712-724.
  • K. M. Sim and C.Y. Choi. Agents that React to Changing Market Situations. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol. 33, No. 2, pp 188-201, April 2003.
  • K. M. Sim. A Market-driven Model for Designing Negotiation Agents. In Computational Intelligence, Special issue in Agent Technology for E-commerce, vol. 18, no. 4, 2002.
other references
Other references
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