In order for the Colored-Trails Agent
to model the opponent, prior knowledge regarding the behavior of humans
is needed. The learning mechanism requires sufficient human data for training and is currently limited to one domain only.
Gal10 also examines automated
agent design in the domain of the
Colored Trails. They present a machine-learning approach for modeling
human behavior in a two-player negotiation, where one player proposes a
trade to the other, who can accept or
reject it. Their model tries to predict
the reaction of the opponent to the different offers, and using this prediction
it determines the best strategy for the
agent. The domain on which Gal et al.
tested their agent can also be viewed as
a Cliff-Edge environment, more complex than the Ultimatum Game, upon
which Katz and Kraus evaluated their
Gal et al. show that the proposed
model successfully learns the social preferences of the opponent and
achieves better results than the Nash
equilibrium, Nash bargaining computer agents, and human players.
We now continue with agents that
are domain-independent, and we propose an agent that has greater generality than the aforementioned agents.
The Guessing Heuristic Agent
Jonker et al.
15 deal with bilateral multi-issue and multi-attribute negotiations
that involve incomplete information.
The negotiation follows the alternating
offer protocol and is conducted once
with each opponent. Jonker designed
a generic agent that uses a “guessing
heuristic” in the buyer-seller domain.a
This heuristic tries to predict the opponent’s preferences based on its offers’
history. This is under the assumption
the opponent’s utility has a linear function structure. Jonker et al. assert that
this heuristic allows their agent to improve the outcome of the negotiations.
Regarding the offer generation mechanism, they use a concession mechanism to obtain the next offer. In their
experiments, the automated agent
a Although Jonker et al. discuss and present
results on one domain only, they state their
model is generic and has also been applied in
if we look into
the design elements
of all the agents
this article, we
cannot find one
that connects them
or can account
for their good
acts as a proxy for the human user.
The user is involved only in the beginning when he inputs the preference
parameters. Then the agent generates
the offers and the counteroffers. When
comparing negotiations involving only
automated agents with negotiations
involving only humans, the agents
usually outperformed the humans (in
the buyer’s role). Yet, in an additional
experiment they matched humans versus agent negotiators. In this experiment, humans only played the role of
the buyer. When comparing the human vs. agent negotiations to that of
only automated agents, the humans
attained somewhat better results than
the agents (in the buyer’s role), based
on the average utilities. The authors
believe this should be accounted to the
fact that humans forced the automated
negotiators to make more concessions
then they themselves did.
The next agent also deals with bilateral multi-issue negotiations that
involve incomplete information. Nonetheless the negotiation protocol is
richer than that of the Guessing Heuristic agent.
The QOAgent24 is a domain-independent agent that can negotiate with
people in environments of finite horizon bilateral negotiations with incomplete information. The negotiations
consider a finite set of multi-attribute
issues and time constraints. Costs are
assigned to each negotiator, such that
during the negotiation process, the
negotiator might gain or lose utility
over time. If no agreement is reached
by a given deadline a status quo outcome is enforced. A negotiator can
also opt-out of the negotiation if it decides that the negotiation is not proceeding in a favorable manner. Similar to the negotiation protocol in the
Diplomat agent’s domain, the negotiation protocol in the QOAgent’s domain
extends the model of alternating offers such that each agent can perform
up to M > 0 interactions with the opponent agent during each time period.
In addition, queries and promises are
allowed that add unenforceable agreements to the environment.
With respect to incomplete information, each negotiator keeps his preferences private, though the preferenc-