Agent-mediated electronic commerce: automated negotiation by Maria Fasli, Onn Shehory

By Maria Fasli, Onn Shehory

This booklet constitutes the completely refereed post-proceedings of the joint overseas Workshops on buying and selling Agent layout and research, TADA 2006, and on Agent Mediated digital trade, AMEC VIII 2006, held in Hakodate, Japan, in may well 2006 as an linked occasion of AAMAS 2006, the fifth foreign Joint convention on independent brokers and Multiagent Systems.

The 17 revised complete papers offered have been rigorously chosen from the shows made on the workshop and contain papers from the once a year TAC event whose function is to stimulate study in buying and selling brokers and marketplace mechanisms via delivering a platform for brokers competing in well-defined industry eventualities. The papers tackle a mixture of either theoretical and sensible concerns in buying and selling agent layout and applied sciences, theoretical and empirical overview of recommendations in advanced buying and selling situations in addition to mechanism layout. additionally coated are problems with agent-mediated digital trade starting from the layout of digital marketplaces and effective protocols to behavioral features of brokers working in such environments.

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Additional info for Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets. AAMAS 2006 workshop, Tada/Amec 2006, Hakodate, Japan, May 9, 2006, selected and revised papers

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Assume an agent a of type i makes an offer at t = 1. If this offer gets rejected, then it means that b is not of type OPTA(i, 1) and so a updates its beliefs about b using Bayes’ rule (excluding passed deadlines and putting all the weight of the posterior distribution of a’s type over all Ni such that i = OPTA(i, 1)). Now, on the basis of a’s offer at t = 1 (say [a1 , b1 ]), b can infer the possible types for a. Thus, b also updates its beliefs using Bayes’ rule (putting all the weight of the posterior distribution of a’s type over N where N ⊆ N is the set of possible types for a that can offer [a1 , b1 ] in equilibrium).

In practice, our providing agents use the same TNS as the buying agent, though each one has his own transformation costs, which in turn are Savings in Combinatorial Auctions Through Transformation Relationships 27 Algorithm 2. G ENERATE -O FFER -V ECTOR[n, m, pbid density , pof f ered units ] 1: for j ← 1 to m do 2: [a1j , . . , n] such that aij = 0 6: u←0 7: while u = 0 do 8: u ← SAMPLE-G EOMETRIC -D ISTRIBUTION [pof f ered units ] 9: aij ← u 10: end while 11: end for 12: end for assessed as a variation of the buying agent’s ones.

Notice too that our integer program can be clearly regarded as an extension of the integer program we must solve for an MUCRA as formalised in [10]. Thus, the second component of expression 9 changes the overall cost as transformations are applied, whereas the second component of expression 10 makes sure that the units of the selected bids fulfil with a buyer’s requirements taking into account the units consumed and produced by transformations. 4 Empirical Evaluation The main purpose of our experiments is to empirically evaluate the benefits of introducing t-relationships in a multi-unit combinatorial reverse auction.

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