environment’s rich settings. They also
state that the negotiation problem can
be addressed more in depth (following
other researchers who have focused
mainly on the negotiation field), rather
than in breadth (as presently conducted in their system).
The Rule of Thumb for
Designing Automated Agents
We should probably begin with the
conclusion. Despite the title of this
section, there may not be a good rule
of thumb for designing automated negotiators with human negotiators. The
accompanying table summarizes the
main contributions made by each of
the reviewed agents. If we look into the
design elements of all the agents mentioned in this article, we cannot find
one specific feature that connects them
or can account for their good negotiation skills. Nonetheless, we can note
several features that have been used in
several agents. Agent designers might
take these features into consideration
when designing their automated agent,
while also taking into account the settings and the environment in which
their agent will operate.
The first feature is randomization,
which was used in Diplomat, QOAgent,
and also (though not explicitly) in the
Cliff-Edge agents. The randomization
factor allows these agents to be more
resilient (or robust) to adversaries that
try to manipulate them to gain better
results on their part. In addition, it allows them to be more flexible, rather
than strict, in accepting agreements
and ending negotiations.
The second feature can be viewed as
a concession strategy. Both the AutONA
agent and the Guessing Heuristic agent
implemented this strategy, which influenced the offer-generation mechanism
of their agent. A concession strategy
might also have a psychological effect
on the opponent that would make it
more comfortable for the opponent to
accept agreements or to make concessions on his own as well.
The last feature common in several
agents is the use of a database. The database can be built on previous interactions with the same human opponent
or for all opponents. The agent consults
the database to better model the opponent, to learn about possible behaviors
and actions and to adjust its behavior
to the specific opponent. A database of
the history can also be used to obtain
information about the behavior of the
opponents, if such information is not
known, or cannot be characterized, in
advance.
Lastly, though not exactly a feature,
but worth mentioning, is that none of
the agents we reviewed implemented
equilibrium strategies. This is an interesting observation and most likely
is due to the fact that these strategies
have been shown to behave poorly
when implemented in automated negotiators matched with human negotiators, mainly due to the complex environment and the bounded rationality
of people. In some cases,
21 experiments
have shown that when the automated
agent follows its equilibrium strategy
the human negotiators who negotiate with it become frustrated, mainly
since the automated agent repeatedly
proposes the same offer, and the negotiation often ends with no agreement.
This has been shown in cases in which
the complexity of finding the equilibrium is low and the players have full
information.
conclusion
In this article we presented the challenges and current state-of-the-art automated solutions for proficient negotiations with humans. Nonetheless we
do not claim that all existing solutions
have been summarized in this article.
We briefly state the importance of automated negotiators and propose suggestions for future work in this field.
The importance of designing an
automated negotiator that can negotiate efficiently with humans cannot be
understated and we have shown that
indeed it is possible to design such
negotiators. By pursuing non-classical methods of decision making and
a learning mechanism for modeling
the opponent it could be possible to
achieve greater flexibility and effective
outcomes. As we have shown, this can
also be accomplished without constraining the model to the domain.
Many of the automated negotiation
agents are not intended to replace humans in negotiations, but rather as an
efficient decision support tool or as
a training tool for negotiations with
people. Thus, such agents can be used
to support training in real-life negotia-
tions, such as: e-commerce and electronic negotiations (e-negotiations),
and they can also be used as the main
tool in conventional lectures or online
courses, aimed at turning the trainee
into a better negotiator.
To date, it seems that research in AI
has neglected the issue of proficiently
negotiating with people, at the expense
of designing automated agents aimed
to negotiate with rational agents or
other automated agents.
39 Others have
focused on improving different heuristics and strategies and the analysis
of game theory aspects (for example,
Kraus20 and Muthoo26). Nonetheless, it
is noteworthy that these are important
aspects in which the AI community
has certainly made an impact. Unfortunately, not much progress has been
made with regard to automated negotiators with people, leaving many unfaced challenges.
suggestions for future Research
The work is far from complete and the
challenges remain exciting. To entice
the reader, we list a few of these challenges here:
The first challenge is to enrich the
negotiation language. Many researchers restrict themselves to the basic
model of alternating offers whereby
the language consists of offers and
counteroffers alone. Rich and realistic negotiations, however, consist of
other types of actions (for example,
threats, comments, promises, and
queries), as well as simultaneous actions (that is, each agent can perform
up to M > 0 interactions with the other
party each time period). It is essential
these actions and behaviors are modeled in the automated negotiators to
allow better negotiations with human
negotiators.
Another challenge, also discussed
previously, is the need for a general-purpose automated negotiator. With
the vast amount of applications and
domains, automated agents cannot
be restricted to one single domain and
must be adaptable to different settings. The trade-off between the performance of a general-purpose automated
negotiator and a domain-dependent
negotiator should be considered and
methods for improving the efficacy of
a general-purpose negotiator should
be sought. Achieving this will also con-
