will it also generate these payoffs when
matched with other automated agents,
which might be more accessible than
human negotiators, and which also exist in open environments?
Generates a maximal combined ˲
payoff for both negotiators, that is, the
agent is more concerned with maximizing the combined utilities than its
Allows most negotiations to end ˲
with an agreement, rather than one
of the sides opting-out or terminating
the negotiations with a status-quo outcome?
Is domain dependent and its tech- ˲
nique suitable only for that domain or
one that is domain independent and
can be adapted to several domains?
This might be an important factor if an
agent is required to adapt to dynamic
settings, for example.
Behave in such a manner that ˲
would leave its counterpart speculating whether it is an automated negotiator or a human one?
In this article we do not define what
or whether there is a best answer. We
also do not claim a best answer indeed
exists. Yet researchers should take these
and other measures into consideration
when designing their agents. Perhaps
certain criteria and benchmarks are in
order to allow an adequate comparison
between automated agents.
Here we review automated agents
that incorporate the two mechanisms
of decision making via modeling human factors and learning the opponent’s model. By doing so they try to
tackle the aforementioned challenges
in bilateral negotiations. While many
automated negotiators’ designs have
been suggested in the literature, we
only review those that have actually
been evaluated and tested with human
counterparts. This is mainly due to the
fact that in order to test the proficiency of an automated negotiator whose
purpose is to negotiate with human
negotiators, one must match it with
humans. It is not sufficient to test it
with other automated agents, even if
they were supposed to have been designed by humans as bounded rational
agents, due to many of the reasons previously mentioned.
Tackling the challenges
Here we describe several automated
agents that try to tackle the challenges
and proficiently negotiate in open environments. All of these agents were evaluated with human counterparts. It is
worth noting that most of these agents
use structured (or semi-structured) language and do not implement any natural language processing methods (with
the one exception of the Virtual Human
agent). In addition, the agents vary with
respect to their characteristics. For example, some are domain-dependent,
while others are domain-independent
and are more general in nature; some
use the history of past interactions to
model the opponent, while others only
have access to current interaction data.
Figure 3 depicts a general architecture
for an automated agent design. We begin by describing the oldest agent of all
of them—the Diplomat agent.
The Diplomat Agent
Over 20 years ago Kraus and Lehmann
developed an agent called Diplomat22
that played the Diplomacy game (see
Figure 4) with the goal to win. The game
involves negotiations in multi-issue
settings with incomplete information
concerning the other agents’ goals,
and misleading information can be exchanged between the different agents.
The negotiation protocol extends the
model of alternating offers and allows
simultaneous negotiations between
the parties, as well as multiple interactions with the opponent agents during
each time period. The issue of trust
also plays an important role, as commitments might be breached. In addition, as each game consists of several
sessions, it can be viewed as repeated
The main innovation of the
Diplomat agent is probably the fact that it
consists of five different modules that
work together to achieve a common
goal. Different personality traits are
implemented in the different modules.
These traits affect the behavior of the
agent and can be changed during each
run, allowing Diplomat to change its
‘personality’ from one game to another
and to act nondeterministically. In addition, the agent has a limited learning
capability that allows it to try to estimate
the personality traits of its rivals (for
example, their risk attitude). Based on
this, Diplomat assesses whether or not
the other players will keep their prom-
ises. In addition, Diplomat incorporates
randomization in its decision-making
component. This randomization, influenced by Diplomat’s personality traits,
determines whether some agreements
will be breached or fulfilled.
The results reported by Kraus and
Lehmann show that Diplomat played
well in the games in which it participated, and most human players were
not able to guess which of the players
was played by the automated agent.
Nonetheless, the main disadvantage
of Diplomat is that it is a domain-dependent agent, that is, suitable only for
the Diplomacy game. Since the game
is quite complex and time consuming
not many experiments were carried out
with human players to validate the results and reach a level of significance.
Yet, at the time Diplomat did open a
new and exciting line of research, some
of which we review here.
We continue with a more recent
agent also constrained to a specific domain and involving single-issue negotiations. However, it takes into account
the history of past interactions to model the opponents.