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adopted. Second, the representation of messages
among agents should be structured into two levels:
communication language level and content language
level, that is, the envelope and the payload, respec-
tively.
2.1 Related Work
Nowdays, there are many interaction protocols de-
fined for tackling the negotiation process. Anthony
P. et al. (Anthony and Jennings, 2003) presented an
heuristic decision making framework to manage the
problem of bidding across multiple auctions, but they
did not define a negotiation protocol specifying the
messages exchanged among agents. A more current
work was developed by Mia M. et al. (Mia et al.,
2005), who exposed a negotiation process without us-
ing any type of standard communication. If we take
the communication problem into account, these works
are incomplete because another software agent can-
not communicate by employing unknown negotiation
protocols in most cases. Skylogiannis et al. (Skylo-
giannis et al., 2005) presented a work in which they
criticized the use of FIPA protocols and proposed
their own negotiation protocol. Although their sys-
tem uses FIPA standard communicative acts, they do
not employ a standard interaction protocol.
Another important research line is related to the
use of rules for specifying the protocol. Bartolini et
al. (Bartolini et al., 2002) justified the inclussion of
negotiation rules to explicitly specify the negotiation
mechanism, controlling additional aspects of the ne-
gotiation (e.g. the criteria for accepting a bid in an
English Auction).
2.2 Our Proposal
In this paper, we propose a solution to a scenario with
many agents, one acting as a buyer and other act-
ing as sellers. This solution is based on the FIPA
Iterated Contract Net Interaction Protocol (Founda-
tion for Intelligent Physical Agents FIPA, 2000), a
protocol in which one agent (the Initiator) takes the
role of manager which wishes to have some task per-
formed by one or more agents (the Participants), and
further wishes to optimize a function that character-
izes the task. In our context, this function measures
the similarity among offered products and the desired
product depending on the characteristics that define
it. We have chosen this protocol because it can be
adapted to our negotiation scenario in a standard way,
as discussed in Section 4. Moreover, we have imple-
mented a FIPA-compliant multi-agent system (Foun-
dation for Intelligent Physical Agents FIPA, 2004) to
test this solution. The main benefit of using this ap-
proach is to obtain a high-interoperable environment
where software agents can be easily integrated for
making proposals in the negotiation process. This
work will be mainly focused on the negotiation proto-
col by specifying the meaning of the communicative
acts exchanged among agents.
3 APPLICATION FRAME
The number of participants is a significant property of
negotiation in E-Commerce (Lomuscio et al., 2003).
In fact, three main negotiation scenarios are distin-
guished attending to the number of participants in-
volved in a negotiation: i) one-to-one, ii) many-to-one
or one-to-many, and iii) many-to-many. The buyer
agent used in this paper was designed to work in a sce-
nario composed of many sellers and only one buyer.
The main goal of the buyer agent is to buy a product
or to acquire a service. In order to reach this aim, the
buyer agent owns a description of such product or ser-
vice through different characteristics (more than one).
In addition, there is a competition among sellers to
make a deal with the buyer agent. This way, the buyer
agent obtains profit from this competition.
The buyer agent’s behaviour consists of several
steps. First, it receives offers from different sellers in
accordance with the product or service desired. Af-
ter comparing these offers, the buyer agent can ac-
cept an offer that is closed enough to the ideal pact.
Otherwise, it orders them depending on the similarity
regarding to the ideal value, making groups of three
offers and showing to the supplier of the worst offer
the other two of the list. The main goal consists of
asking this last supplier for changing some character-
istics or adding new characteristics that make its offer
better than the others. By means of this process, the
buyer agent obtains profit of the competition among
sellers (Castro-Schez et al., 2004b).
The first stages of the negotiation often involves
imprecise terms before reaching a deal. On the one
hand, in these stages both the buyer and sellers do
not know all the outstanding information of negoti-
ation. On the other hand, this knowledge increases
as the negotiation process evolves. For these reasons,
we should provide agents with an intuitive and for-
mal way of representing both precise and imprecise
values. In this work, we use several data types for
representing these restrictions (Castro-Schez et al.,
2004a): ordered-discrete or ordinal (e.g. product
state), unordered-discrete or nominal (e.g. supplier’s
country), boolean (e.g. new manufacture), ranking
(e.g. quality), and continuous or numerical (e.g.
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