Computational Intelligence, Volume 0, Number 0, 2012

GENIUS: AN INTEGRATED ENVIRONMENT FOR SUPPORTING THE
DESIGN OF GENERIC AUTOMATED NEGOTIATORSRAZ LIN,1 SARIT KRAUS,1,2 TIM BAARSLAG,3 DMYTRO TYKHONOV,3 KOEN HINDRIKS,3
AND CATHOLIJN M. JONKER3
1
Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel
Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland, USA
3
Man-Machine Interaction Group, Delft University of Technology, Mekelweg, Delft, the Netherlands
2

The design of automated negotiators has been the focus of abundant research in recent years. However, due
to difficulties involved in creating generalized agents that can negotiate in several domains and against humancounterparts, many automated negotiators are domain specific and their behavior cannot be generalized for otherdomains. Some of these difficulties arise from the differences inherent within the domains, the need to understandand learn negotiators’ diverse preferences concerning issues of the domain, and the different strategies negotiators
can undertake. In this paper we present a system that enables alleviation of the difficulties in the design processof general automated negotiators termed GENIUS, a General Environment for Negotiation with Intelligent multipurpose Usage Simulation. With the constant introduction of new domains, e-commerce and other applications,which require automated negotiations, generic automated negotiators encompass many benefits and advantages overagents that are designed for a specific domain. Based on experiments conducted with automated agents designed byhuman subjects using GENIUS we provide both quantitative and qualitative results to illustrate its efficacy. Finally,
we also analyze a recent automated bilateral negotiators competition that was based on GENIUS. Our results show
the advantages and underlying benefits of using GENIUS and how it can facilitate the design of general automatednegotiators.
Received 28 November 2010; Revised 25 October 2011; Accepted 6 November 2011
Key words: agents competition, automated negotiation, human/computer interaction, bilateral negotiation.

1. INTRODUCTION
One cannot understate the importance of negotiation and the centrality it has taken
in our everyday lives, in general, and in specific situations in particular (e.g., in hostagecrises situations as described in Kraus et al. (1992)). The fact that negotiation covers many
aspects of our lives has led to extensive research in the area of automated negotiators, that
is, automated agents capable of negotiating with other agents in a specific setting.
There are several difficulties that emerge when designing automated negotiating agents,that is automated programs with negotiating capabilities. First, although people can negotiate
in different settings and domains, when designing an automated agent a decision should bemade whether the agent should be a general purpose negotiator, that is, domain-independent(e.g., Lin et al. 2008) and able to successfully negotiate in many settings or suitable for only
one specific domain (e.g., Ficici and Pfeffer (2008) for the Colored Trail domain, or Kraus
and Lehmann (1995) for the Diplomacy game). There are obvious advantages of an agent’s
specificity in a given domain. It allows the agent designers to construct strategies that enable
better negotiation compared to strategies for a more general purpose negotiator. However, thisis also one of the major weaknesses of these types of agents. With the constant introductionof new domains, e-commerce and other applications that require negotiations, the generalityof an automated negotiator becomes important, because automated agents tailored to specificdomains are useless when they are used in the new domains and applications.

Address correspondence to Raz Lin, Department of Computer Science, Bar-Ilan University, Ramat-Gan, Israel 52900;
e-mail: razlin81@gmail.com
C 2012 Wiley Periodicals, Inc.

COMPUTATIONAL INTELLIGENCE

Another difficulty in designing automated negotiators concerns open environments, suchas online markets, patient care delivery systems, virtual reality, and simulation systems used
for training (e.g., the Trading Agent Competition (TAC) reported by Wellman, Greenwald,
and Stone (2007)). These environments lack a central mechanism for controlling the agents’behavior, where agents may encounter human decision makers whose behavior is diverse.
Such diverse behavior cannot be captured by a monolithic model; humans tend to makemistakes because they are affected by cognitive, social, and cultural factors, etc. (Bazerman
and Neale 1992; Lax and Sebenius 1992).
Although the two aforementioned difficulties should be dealt with in more detail, in thispaper we do not focus on the design of an efficient automated negotiator; we do not evenclaim that we have the right “formula” to do so. We do, however, present an environment
to facilitate the design and evaluation of automated negotiators’ strategies. The environment, GENIUS, is a General Environment for Negotiation with Intelligent multi-purpose
Usage Simulation. To our knowledge, this is the first environment of its kind that both assists in the design of strategies for automated negotiators and also supports the evaluation
process of the agent. Thus, we believe this environment is very useful for agent designers
and can take a central part in the process of designing automated agents. Although designing agents can be done in any agent-oriented software engineering methodology, GENIUS
wraps this in an easy-to-use environment and allows the designers to focus on the development of strategies for negotiation in an open environment with multiattribute utility
functions.
GENIUS incorporates several mechanisms that aim to support the design of a generalautomated negotiator. The first mechanism is an analytical toolbox, which provides a variety
of tools to analyze the performance of agents, the outcome of the negotiation, and its
dynamics. The second mechanism is a repository of domains and utility functions. Finally,it also comprises repositories of automated negotiators. A comprehensive description of thetool is provided in Section 3.
In addition, GENIUS enables the evaluation of different strategies used by automatedagents that were designed using the tool. This is an important contribution as it allows
researchers to empirically and objectively compare their agents with others in different
domains and settings and validate their results. This in turn allows to generate better automated negotiators, explore different learning and adaptation strategies and opponent models,and collect state-of-the-art negotiating agents, negotiation domains, and preference profiles. It also enables making them available and accessible for the negotiation researchcommunity.
To verify its efficacy, GENIUS was introduced to students, who were required to design
automated agents for different negotiation tasks. Their agents were evaluated and bothquantitative and qualitative results were gathered. A total of 65 automated agents were
designed by 65 students. We describe the experimental methodology and results in Section 4.
The results support our claim that GENIUS helps and supports the design process of an
automated negotiator, from the initial design, through the evaluation of the agent, and redesign
and improvements, based on its performance.
In May 2011 we organized the first automated negotiating agents competition (ANAC),
with the aim of coordinating the research into automated agent design and proficient bidding
strategies for bilateral multiissue closed negotiation, similar to the objective achieved by the
TAC for the trading agent problem (Wellman et al. 2007). The entire competition was basedon the GENIUS environment. We analyze the benefits of using GENIUS for this competitionin Section 5.
We begin by reviewing related research relating to the design of general automatednegotiators.

GENIUS: SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORS

2. RELATED WORK
Research on general agent negotiators has given rise to a broad variety of such agents.
The strategies of the agents usually vary from equilibrium strategies through optimal approaches to heuristics. Here we focus in particular on agents that are able to conduct bilateralnegotiations with incomplete information. Examples of such general agent negotiators in theliterature include, among others, Sycara and Zeng (1996), who introduce a generic agent
called Bazaar, Faratin, Sierra, and Jennings (2002), who propose an agent that is able to
make trade-offs in negotiations and is motivated by maximizing the joint utility of the outcome (that is, the agents are utility maximizers that seek Pareto-optimal agreements), Karpet al. (2003), who take a game-theoretic view and propose a negotiation strategy based on
game-trees, Jonker, Robu, and Treur (2007), who propose a negotiation model called AgentBased Market Places (ABMP), and Lin et al. (2008), who propose an agent negotiator called
QOAgent. All of these agents are proposed as domain-independent agents. The motivation
for introducing these agents, however, has varied and has related to diverse topics, such aslearning in negotiation, the use of various heuristics, or negotiating with people. Typically,
alternating offer protocols are used where agents exchange offers in turn (Rubinstein 1982),
sometimes with minor modifications, for example, Lin et al. (2008) proposed. Lomuscio,
Wooldridge, and Jennings (2001) in their work, offer a useful classification of types of agent
negotiators. Nonetheless, the importance and contribution of GENIUS is that it provides, inaddition to the design of domain-independent agents, a general infrastructure for designing
such agents, defining the domains and evaluating based on other agents developed in thesame infrastructure. GENIUS was built in the intent to be publicly available in the aim ofproviding researchers a simple and effective tool for designing negotiations’ strategies.
As we argue that a generic environment for designing and evaluating agent negotiators isuseful, we briefly review related work that is explicitly aimed at the evaluation of various agent
negotiators. Most of the work reported herein concerns the evaluation of various strategies
for negotiation used by such agents. Although some results were obtained by game-theoretic
analysis (e.g., Rosenschein and Zlotkin 1994; Kraus 2001), most results were obtained bymeans of simulation (e.g., Devaux and Paraschiv 2001; Fatima, Wooldridge, and Jennings
2005; Henderson et al. 2008). Devaux and Paraschiv (2001) present work that compares
agents negotiating in the Internet agent-based markets. In particular, they compare a strategyof their own agent with behavioral-based strategies taken from the literature (Faratin, Sierra,
and Jennings 1998). The simulations are performed in an abstract domain where agentsneed to negotiate the price of a product. Similarly, Henderson et al. (2008) present resultsof the performance of various negotiation strategies in a simulated car hire scenario. Finally,
Matos, Sierra, and Jennings (1998) conducted experiments to determine the most successful
strategies using an evolutionary approach in an abstract domain called the service-oriented
domain.
Even though several of the approaches mentioned use of a rather abstract domain with arange of parameters that may be varied, we argue that the focus on a single domain in most simulations makes those simulations limited. A similar argument to this end has been put forward
in Hindriks and Tykhonov (2008b). The analysis of agent negotiators in multiple domains
may significantly improve the performance of such agents. To the best of our knowledge,
this is the first time that quantitative and qualitative evidence is presented to substantiate thisclaim.
Manistersky, Lin, and Kraus (2008) discuss how people who design agent negotiatorschange their design over time. They study how students changed their design of a trading agent
that negotiates in an open environment. After initial design of their agents, human designers
obtained additional information about the performance of their agents by receiving logs of

COMPUTATIONAL INTELLIGENCE

negotiations between their agents and agents designed by others. These logs provided themeans to analyze the negotiation behavior, and an opportunity to improve the performanceof the agents. The GENIUS environment discussed here provides a tool that supports suchanalysis, subsequent improvement of the design, and structures the enhancement process.
With regard to systems that facilitate the actual design of agents or agent strategies innegotiations, few systems are close to our line of suggested work. Most of the systems that
may be somewhat related to the main focus of our paper are negotiation support systems(e.g., the Interactive Computer-Assisted Negotiation Support system (ICANS) presented
in Thiessen, Loucks, and Stedinger (1998), the InterNeg Support Program for Intercultural
REsearch (INSPIRE)), however, GENIUS advances the state-of-the-art by also providing evaluation mechanisms that allow a quick and simple evaluation of strategies and the facilitationof automated negotiator’s design. INSPIRE, by Kersten and Noronha (1999), is a Web-basednegotiation support system with the primary goal of facilitating negotiation research in aninternational setting. The system enables negotiation between two people, collects data about
negotiations, and has some basic functionality for the analysis of the agreements, such as
calculation of the utility of an agreement and exchanged offers. However, unlike GENIUS,
it does not allow integration of an automated negotiating agent and thus does not include
repositories of agents as we propose. Perhaps Neg-o-Net (Hales 2002) is more similar to
GENIUS than all the other support systems. The Neg-o-Net model is a generic agent-based
computational simulation model for capturing multiagency negotiations concerning resourceand environmental management decisions. The Neg-o-Net model includes both a negotiation algorithm and some agent models. An agent’s preferences are modeled using digraphs(scripts). Nodes represent states of the agent that can be achieved by performing actions(arcs). Each state is evaluated using utility functions. The user can modify the agent’s script
to model his/her preferences with regard to states and actions. Although Neg-o-Net is muchsimilar to GENIUS, it has two downsizes. First, they currently do not support the incorporationof human negotiators, but only automated ones. Second, they do not provide any evaluation
mechanism of the strategies as GENIUS provides.
3. THE GENIUS SYSTEM
The aim of the environment, that is GENIUS, is to facilitate the design of negotiation
strategies. Using GENIUS, programmers can focus mainly on the strategy design. This isachieved by GENIUS providing both a flexible and easy-to-use environment for implementing
agents and mechanisms that support the strategy design and analysis of the agents. Moreover,the core of GENIUS can be incorporated in a larger negotiation support system that is able
to fully support the entire negotiation from beginning to end (examples include the Pocket
Negotiator, Hindriks and Jonker (2008), and an animated mediator, Lin, Gev, and Kraus(2011)).
GENIUS is focused on bilateral negotiation, i.e., a negotiation between two parties oragents A and B. The agents negotiate over issues that are part of a negotiation domain, andevery issue has an associated range of alternatives or values. A negotiation outcome consistsof a mapping of every issue to a value, and the set  of all possible outcomes is called theoutcome space. The outcome space is common knowledge to the negotiating parties andstays fixed during a single negotiation session.
We further assume that both parties have certain preferences prescribed by a preference
profile over . These preferences can be modeled by means of a normalized utility function
U , which maps a possible outcome ω ∈  to a real-valued number in the range [0, 1]. In
contrast to the outcome space, the preference profile of the agents is private information.

GENIUS: SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORSFIGURE 1. GENIUS’s architecture.

Finally, the interaction between negotiating parties is regulated by a negotiation protocolthat defines the rules of how and when proposals can be exchanged.
We begin by describing the detailed and technical architecture of GENIUS and continue
with a description of its usage by researchers.
3.1. GENIUS’s Architecture
GENIUS provides a flexible simulation environment. Its architecture, presented in
Figure 1, is built from several modules: (a) analysis, (b) repository, (c) logging, and (d)simulation control. The analysis module provides researchers the option to analyze the outcomes using different evaluation metrics. The repository contains three different modules of
the negotiation that interact with three analysis modules built into GENIUS:(1) Negotiation scenarios, consisting of a negotiation domain with at least two preference
profiles defined on that domain. When a negotiation scenario has been specified, GENIUS
is able to perform outcome space analysis on the scenario;(2) Negotiating agents that implement the Agent Application Programming Interface (API).
Agent introspection allows the agents to sense the negotiation environment;(3) Negotiation protocols, both one-to-one, and multilateral. Depending on the particular protocol, GENIUS can provide negotiation dance analysis to evaluate negotiation
characteristics such as fairness, social welfare, and so on.
Finally, the simulation control and logging modules allow researchers to control thesimulations, debug it, and obtain detailed information.

COMPUTATIONAL INTELLIGENCEFIGURE 2. GENIUS’s detailed architecture.

This is reflected in the detailed architecture presented in Figure 2. The top of the diagram
represents the User Interface Layer, in which the user can specify its preferences during the
preparation and exploration phase of the negotiation. The Negotiation Ontology Layer below
helps to represent the negotiation domain and preference profiles. Currently, linear utility
functions are used to model preferences but additional representations can be integrated
into the system. The negotiation ontology can be accessed by the Agent Layer to retrieve
all relevant negotiation factors pertaining to a particular negotiation scenario. Finally, at thebottom of the diagram, the Negotiation Environment Layer defines the interaction protocol
between the agents.
3.2. GENIUS as a Tool for Researchers
GENIUS enables negotiation between automated agents, as well as people. In this section
we describe the use of GENIUS before the negotiation, during the negotiation, and afterward.
3.2.1. Preparation Phase.
For automated agents, GENIUS provides skeleton classes tohelp designers implement their negotiating agents. It provides functionality to access information about the negotiation domain and the preference profile of the agent. An interaction
component of GENIUS manages the rules of encounter or protocol that regulates the agent’s
interaction in the negotiation. This allows the agent designer to focus on the design of theagent, and eliminates the need to implement the communication protocol or the negotiation
protocol. Existing agents can be easily integrated in GENIUS by means of adapters.1
1 Indeed as was shown in Hindriks, Jonker, and Tykhonov (2008).

GENIUS: SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORSFIGURE 3. Variations of the negotiation settings.

When designing an automated agent, the designer needs to take into account the settings
in which the agent will operate. The setting determines several parameters which dictate
the number of negotiators taking part in the negotiation, the time frame of the negotiation,and the issues on which the negotiation is being conducted. The negotiation setting also
consists of a set of objectives and issues to be resolved. Various types of issues can be
involved, including discrete enumerated value sets, integer-value sets, and real-value sets.
The negotiation setting can consist of noncooperative or cooperative negotiators. Generally speaking, cooperative agents try to maximize their combined joint utilities (e.g., see
Zhang, Lesser, and Podorozhny 2005) whereas noncooperative agents try to maximize theirown utilities regardless of the other sides’ utilities. Finally, the negotiation protocol defines the formal interaction between the negotiators: whether the negotiation is done only
once (one shot) or repeatedly, and how the exchange of offers between the agents is conducted. In addition, the protocol states whether agreements are enforceable or not, andwhether the negotiation has a finite or infinite horizon. The negotiation is said to have afinite horizon if the length of every possible history of the negotiation is finite. In this respect, time costs may also be assigned and they may increase or decrease the utility of thenegotiator.
Figure 3 depicts the different variations in the settings. GENIUS provides a test bed whichallows the designer to easily vary and change these negotiation parameters.
Using GENIUS, a researcher can setup a single negotiation session or a tournament via
the graphical user interface (GUI) simulation (see Figure 4) using the negotiation domains
and preference profiles from a repository (top left corner of the GUI simulation), and choose
strategies for the negotiating parties (bottom left corner of the GUI simulation, as indicatedby the “Agents” label). For this purpose, a GUI layer provides options to create a negotiation
domain and define agent preferences. This also includes defining different preferences foreach role.
A preference profile specifies the preferences regarding possible outcomes of an agent.
This can be considered a mapping function that maps the outcomes of a negotiation domainon the level of satisfaction of an agent associated with that outcome. The structure ofa preference profile, for obvious reasons, resembles that of a domain specification. The
tree-like structure enables specification of relative priorities of parts of the tree. Figure 5
demonstrates how a preference profile can be modified using GENIUS.
Seven negotiation domains are currently available in the repository of GENIUS. Eachdomain has at least two preference profiles required for bilateral negotiations. The number
of issues in the domains ranges from 3 to 10, where the largest negotiation domain in therepository is the AMPO vs. City taken from Raiffa (1982), and has over 7,000,000 possible agreements. Issues in the repository have different predictabilities of the evaluation

COMPUTATIONAL INTELLIGENCEFIGURE 4. An example of GENIUS’s main user interface, showing the results of a specific negotiation session.

FIGURE 5. Setting the preference profile.

of alternatives. Issues are considered predictable when even though the actual evaluation
function for the issue is unknown, it is possible to guess some of its global properties (formore details, see Hindriks, Jonker, and Tykhonov 2007; Hindriks and Tykhonov 2008b).
The repository of strategies currently contains six automated negotiation strategies, such as
the ABMP strategy by Jonker and Treur (2001), the Zero-Intelligence strategy by Hindriks
et al. (2007), the QO-strategy by Lin et al. (2008), the Bayesian strategy by Hindriks andTykhonov (2008a), and others. The repositories of domains and of agents allow agent designers to test their agents on the different domains and against different kinds of agents andstrategies.
3.2.2. Negotiation Phase.
Human negotiators and automated ones can be joined ina single negotiation session. Human negotiators interact with GENIUS via a GUI. GUIs
included in GENIUS allow the human negotiator to exchange offers with his/her counterpart,GENIUS: SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORSFIGURE 6. An example of the GUI interface of GENIUS for human negotiators during a specific negotiation
session.

FIGURE 7. Rating of the helpfulness of the analytical toolbox.

to keep track of them, and consult with his/her own preference profile (that is, a utility scoreassigned to each issue of the negotiation) to evaluate the offers. Figure 6 depicts an example
of a human negotiator GUI, whereas Figure 8 presents GENIUS’s GUI of a tournament session,
which allows several agents to be matched against each others.

COMPUTATIONAL INTELLIGENCEFIGURE 8. Setting up a tournament session for ANAC 2010 involved choosing a protocol, the participating
agents, and appropriate preference profiles.

3.2.3. Postnegotiation Phase.
GENIUS provides an analytical toolbox for evaluating
negotiation strategies (as shown in the Statistical Toolbox module in the right corner of
Figure 2). This allows to review the performance and benchmark results of negotiators(whether humans or automated) that negotiated using the system. The toolbox calculates
optimal solutions, such as the Pareto efficient frontier, Nash product, and Kalai–Smorodinsky(Raiffa 1982). These solutions are visually shown to the negotiator or the designer of theautomated agent, as depicted in the top right corner of Figure 4. We can see all the possible
agreements in the domain (all dotted areas), where the highest and most right lines denotethe Pareto efficient frontier. During the negotiation each side can see the distance of its
own offers from this Pareto frontier as well as the distance from previous offers (as shownby the two lines inside the curve). Also, the designer can inspect both agents’ proposals
using the analytical toolbox. We note, however, that the visualization of the outcome space
together with the Pareto frontier is only possible when we have complete information of both
negotiating parties, i.e., both negotiating parties have been assigned a preference profile. In
particular, the agent themselves are not aware of the opponent utility of bids in the outcome
space and do not know the location of the Pareto frontier. The researcher however, is presented
the external overview provided by GENIUS that combines the information of both negotiation
parties.
Using the analytical toolbox one can analyze the dynamic properties of a negotiation
session, such as a classification of negotiation moves (a stepwise analysis of moves) and thesensitivity to a counterpart’s preferences measure, as suggested by Hindriks et al. (2007). For
example, one can see whether his/her strategy is concession oriented, i.e., steps are intended
to be concessions, but in fact some of these steps might be unfortunate, namely, althoughfrom the receiver’s perception the proposer of the offer is conceding, the offer is actually

GENIUS: SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORS

worse than the previous offer. The result of the analysis can help agent designers improvetheir agents.
Moreover, negotiating agents designed using heuristic approaches need extensive evaluation, typically through simulations and empirical analysis, as it is usually hard to predict
precisely how the system and the constituent agents will behave in a wide variety of circumstances. To do so, there is a genuine need for the development of a best practice repository
for negotiation techniques. That is, a coherent resource that describes which negotiation
techniques are best suited to a given type of problem or domain. Repositories of agentsand negotiation domains available in GENIUS make it an attractive tool for test bedding
negotiating agents. To steer the research in the direction of negotiating agents an ANAC wasorganized2 using the GENIUS environment, as described in Section 5.
4. EXPERIMENTS
The experiments described below were conducted to test the efficacy of the mechanisms incorporated in GENIUS. Human subjects were instructed to design automatedagents that will negotiate with other automated agents in a tournament in an open environment. The experiments were conducted in several phases to validate the results.
These experiment results show that GENIUS allows creating efficient general automatednegotiators. In the following subsections we describe the negotiation domains, the experimental methodology, and we review the results. We begin by presenting the negotiation
domains.
4.1. Experimental Domain
Although the first experiment was only run on one domain, the second experiment was
run on three domains. In the first two domains we modeled three possible agent types, andthus a set of six different utility functions was created for each domain. In the third domain
only one type was possible for the different roles. The different types of agents describe thedifferent approaches toward the negotiation process and the other party. For example, thedifferent approaches can describe the importance each agent associates with the effects of
the agreement over time. One agent might have a long-term orientation regarding the final
agreement. This type of agent would favor agreements concerned more with future outcomesof the negotiations, than those focusing only on solving the present problem. On the other
hand, another agent might have a short-term orientation which focuses on solving only theburning issues under negotiation without dealing with future aspects that might arise from thenegotiation or its solutions. Finally, there can also be agents with a compromise orientation.
These agents try to find the middle grounds between the possible agreements.
Each negotiator was assigned a utility function at the beginning of the negotiations
but had incomplete information. The incomplete information is expressed as uncertainty
regarding the utility preferences of the opponent regarding the. That is, the different possible
utility functions of the counterpart were public knowledge, but its exact utility function was
unknown.
The first two domains are taken from Lin et al. (2008), in which they were used fornegotiations by human negotiators as well as automated ones. The third domain is takenfrom the Dispute Resolution Research Center at Kellogg School of Management.
2 For more details on the ANAC competition see: http://mmi.tudelft.nl/anac.

COMPUTATIONAL INTELLIGENCETABLE 1. Varying Variables in the Experiments.

Experiment
1
2

Independent variable

Dependent variable

Availability of the analytical toolbox
Availability of additional domains and agents

Negotiator’s strategy

The first domain involved reaching an agreement between Britain and Zimbabwe evolving from the World Health Organization’s Framework Convention on Tobacco Control, theworld’s first public health treaty. The principal goal of the convention is “to protect present
and future generations from the devastating health, social, environmental, and economic
consequences of tobacco consumption and exposure to tobacco smoke.” In this domain, four
different attributes are under negotiation, resulting in a total of 576 possible agreements.
In the second domain, a negotiation takes place after a successful job interview between
an employer and a job candidate. In the negotiation both the employer and the job candidate
wish to formalize the hiring terms and conditions of the applicant. In this scenario, fivedifferent attributes are negotiable with a total of 1,296 possible agreements.
The last domain involves finalizing a project plan between Bob and Alice. In contrast
to the other two domains, in this domain the utility preferences of both sides are completely
symmetric. For each issue, five possible values are negotiable. This is also the largest
scenario of all three, in terms of possible agreements. In this scenario, a total of 15,625
possible agreements exist. Yet, unlike the previous domains, only one type for each role waspossible.
4.2. Experimental Methodology
We evaluated the process of the agents design by requiring computer science undergraduate and graduate students to design automated agents. These agents were matched twice
in a tournament with all other agents. To validate the efficacy of the two different mechanisms available in GENIUS—the analytical toolbox and the repositories of domains andagents—after each tournament, the students were exposed to only one of these mechanisms
and were allowed to redesign their agent. Then, they were matched again in a tournament.
In addition, after the students submitted their new agents, they were required to fill inquestionnaires and evaluate the design process of their agents.
We conducted two experiments, as summarized in Table 1. In the first, we evaluated theefficacy of the analytical toolbox. The second experiment was designed to enable evaluationof the efficacy of the domain and agent repositories. We describe both experiments in thefollowing subsections.
The experiments involved bilateral negotiation and were based on the alternating offer
protocols, in which offers are exchanged in turns (Rubinstein 1982). All domains had afinite horizon, that is, the length of every possible history of the negotiation is finite withincomplete information about the preferences of the counterpart. The negotiation involvedof a finite set of multiattribute issues and time constraints, depending on the domain. During
the negotiation process, the agents might gain or lose utility over time. If no agreement isreached by the given deadline a status quo outcome is enforced. The negotiation can also end
if one of the parties opts out, thus forcing the termination of the negotiation. When running
the automated agents in the tournament, we assigned to each agent a specific utility function,such that in each tournament a different utility function was used.

GENIUS: SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORS

4.2.1. Evaluation of the Analytical Toolbox.
In the first experiment, 51 undergraduate
students were required to design an automated negotiator using the GENIUS environment. The
aim of this experiment was to evaluate the efficacy of the evaluation metrics embedded in theanalytical toolbox as a key for improving negotiators’ strategies. The independent variablein this experiment was the availability (or lack of it) of the analytical toolbox, although
the dependent variable was the automated agent designed by the students.
The experiment was conducted as follows. The students were instructed to design anautomated negotiator which would be able to negotiate in several domains, however, they
were only given the Job Candidate domain described in Section 4.1 as an example. In
addition, three automated negotiators were supplied with the tool:3(1) An agent that follows the Bayesian strategy (Hindriks and Tykhonov 2008a);(2) Another automated agent that follows the ABMP strategy, which is a concessionoriented negotiation strategy (Jonker and Treur 2001), though, the strategy itself was
not explained to the students;(3) A simple agent that sorts all possible offers according to their utility and sends themone-by-one to the opponent starting with the highest utility.
In the first phase, the students were unaware of the analytical toolbox (which was also
removed from the environment and the code). After the students submitted their agent, they
were given an upgraded environment which included the analytical toolbox. They were given
an explanation about its features. Though, the domain involved incomplete information, they
were explained that the visualization is based on complete information and they can evaluate
it each time based on a specific preference profile of their counterpart. Then they wereallocated several days in which they could use it to redesign their agent.
The students’ agents were evaluated three times. The first time included runningthe first phase agents against all other agents. Thus, each agent was matched against
all 51 agents (including itself), each time under a different role. That is, each agent
participated in 102 negotiations, and a total of 5,202 simulations were executed. The
second time, each revised agent was matched against all 51 revised agents (including itself). This allowed us to validate the efficacy of the analytical toolbox by comparing the performance of each revised agent to its original performance. The thirdtime included running the revised agents against each other using a new domain, theBritain–Zimbabwe domain, of which they were unaware during the design process. This
allowed us to evaluate whether the analytical toolbox itself is sufficient for designing genericagents.
4.2.2. Evaluation of the Domain and Agent Repositories.
In this experiment, as in theprevious experiment, 14 graduate students were required to design an automated negotiatorusing the GENIUS environment. The aim of this experiment was to evaluate the efficacy of
existence of domains for generating efficient domain-independent negotiation’s strategies.
The independent variable in this experiment was the availability (or lack of it) of several
domains, although the dependent variable, as in the previous experiment, was the automatedagent designed by the students.
The students were aware of the fact that their agent will be matched with all otherautomated negotiators. Throughout the design process they were unaware of the analytical
toolbox. In the first part of the exercise they were given the Job Candidate domain as
3 The agents were supplied with their code to also demonstrate to the students the use of skeleton classes.

COMPUTATIONAL INTELLIGENCETABLE 2. Average Utility Values Gained by the Automated Agents Before and After Being Exposed to theAnalytical Toolbox.

Approach/role

EmployerJob candidate

Original agents
Revised agents

517
525

490
505

an example. After their submissions, they were given an additional domain, the Britain–
Zimbabwe domain described in Section 4.1. As in the previous experiment, they wereallocated several days in which they could redesign their agents based on the newly introduced
domain. Furthermore, half of the students were given logs of all their matches during thetournament. The logs included detailed information of the negotiation process.
In this experiment the students’ agents were evaluated four times. The first time included
running the first phase agents against all other agents. Thus, each agent was matched against
all 14 agents (including itself). The agents were run twice, once on the domain that was
known to them during the design of the original agents, i.e., the Job Candidate domain, andonce in the Britain–Zimbabwe domain of which they were unaware at the time. The second
time, each revised agent was matched against all 14 revised agents in the Job Candidate
domain and in the Britain–Zimbabwe domain, respectively. This allowed us to validate theefficacy of both the introduction of a new domain and the usage of logs of past negotiations
by comparing the performance of each revised agent to its original performance. Finally, we
ran the students’ agents against each other using a new domain, the Class Project domain, of
which the designers were unaware during the entire design process. Again, we ran both theoriginal agents and the revised agents. This allowed us to evaluate whether or not the two
given domains were sufficient for designing efficient generic agents.
4.3. Experimental Results
The main goal of the experiments was to verify that the mechanisms in GENIUS assist in
alleviating the difficulties in designing efficient general automated negotiators.
As we mentioned earlier, we experimented in three distinct domains. The utility values
ranged from −575 to 895 for the Britain role and from −680 to 830 for the Zimbabwe role;in the Job Candidate domain from 170 to 620 for the employer role and from 60 to 635 forthe job candidate role, and in the Class Project domain from 0 to 29,200 for both sides.
4.3.1. Experiments with the Analytical Toolbox.
We evaluated the design of the agentsusing both quantitative results and qualitative results. The quantitative results, presented inTable 2, comprise a comparison of the agents’ performance in the different settings ofthe experiments, although the qualitative results were gathered from the questionnaires the
subjects filled in after the submission of the revised agents.
The average utility gained by all the revised agents was 525 when playing the role of theemployer and 505 when playing the role of the job candidate. These averages are significantly
higher (using t-test with p-value < 0.001) in both roles compared to the average utilities ofthe original agents (517 and 490, respectively).
To assess the ease of use of the GENIUS environment in creating generic agents, as well as
the helpfulness of the analytical toolbox, the students were asked to answer several questions
on a questionnaire they were administered. Note that, 67% of the students indicated that they

GENIUS: SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORSTABLE 3. Average Utility Values Gained by the Automated Agents before and after Being Exposed to Past
Negotiation Logs.

Approach/role

EmployerJob candidate

Original agents
Revised agents

363
384.29336.8
365.78TABLE 4. Average Utility Values Gained by the Automated Agents before and after Being Exposed to an
Additional Domain.

Britain–Zimbabwe domain
Approach/role
Original agents
Revised agents

Britain302.11369.99Zimbabwe−413.57−377.37Class Project domain
Approach/role
Original agents
Revised agentsBob
11,357
13,348

Alice
10,655
12,113

redesigned their agent in the second part, after being introduced to the analytical toolbox,
and 79.6% used it to gain a better understanding of the negotiation and to redesign theiragents. Moreover, on a scale of 1 (being the lowest) to 7 (being the highest), the students
rated the helpfulness of the tool in understanding the dynamics of the negotiation and thestrategy of their agent at an average of 4.06. The students indicated that the tool enabled themto attain a clearer view of the negotiation dynamics by visualizing the spectrum of offers
and their utilities, and understand which offers to accept and which offers to propose. Somestudents also commented that the tool helped them verify that their implemented strategy
was indeed as they had intended it to be.Figure 7 presents the total rating the students gave
for the helpfulness of the analytical toolbox.
Although this encouraged us, as to the efficacy of the analytical toolbox as a supporting
mechanism for designing automated negotiators, we still needed to verify whether it could
also assist in the design of generic automated negotiators. To test the generality of theagents, we ran the revised agents in a new domain, the Britain–Zimbabwe domain, of which
the students were unaware. However, in this domain only 32.3% of the negotiations werecompleted successfully, i.e., with a full agreement, compared to almost double the amountof negotiations completed successfully on the known domain (64.4%). That is, although theanalytical toolbox was indeed helpful to the students and assisted them in the design of theiragent, it was not sufficient for them to design an efficient generic agent. Thus, we continued
to devise a second experiment with repositories of domains and agents. The results of thisexperiment are described in the next subsection.
4.3.2. Experiments with Domain and Agent Repositories.
We continued to test otheraspects of GENIUS to see whether they help in the design process of agents’ strategies. The
results are summarized in Tables 3 and 4. In the first part, the students were required todesign a generic agent; however, only one domain was given to them. The average utility

COMPUTATIONAL INTELLIGENCE

scores of their agents in the Job Candidate domain were 363 for the employer role and 336.8
for the job candidate role. To evaluate the improvement of the agents due to the logs ofpast negotiations in which they were matched with all other agents, we continued to run thestudents’ revised agents in the same domain. The results of the agents in this experiment were
better, yet not statistically significant (an average utility of 384.29 with a p-value < 0.07 and365.78 with a p-value < 0.06 for the employer and the job candidate roles, respectively).
In addition, significantly more negotiations ended with a full agreement (77.3% in the firststage, as compared to 85% in the second stage, p-value < 0.05).
With respect to using the agent repositories as a means of improving an agent’s strategy,
80% of the students who received the logs of their agents’ past negotiations indicated thatthey indeed used it to improve their agents’ behavior. Some noted, thanks to the logs, that they
had bugs in their strategy or that their agents’ behavior was too strict and less compromising,leading to excessive negotiations which ended in opting out. Using this insight, they revised
their agents’ behavior.
To evaluate the benefits of the domain repositories to the performance of their agent, wefirst matched the students’ original agents against each other in the new Britain–Zimbabwe
domain. Recall that the original agents were designed without knowledge about the new
domain. We then compared these results with the results of the revised agents that had
knowledge of the new domain. The average utility scores of the original agents were 302.11
for the Britain role and −413.57 for the Zimbabwe role. The results of the revised agentswere significantly better in the case of Britain (an average utility of 369.99 with a p-value< 0.03), although the utility was better, though not statistically significant, for the roleof Zimbabwe (−377.37). However, with the revised agents significantly more negotiations
ended with a full agreement (39.2% in the first stage compared to 50.5% in the second stage,p-value < 0.02).
To validate these results, the students’ agents were then run in the Class Project domain,
described in Section 4.1, of which they were unaware during their entire design process.
We first ran the original agents in that domain and the average utility scores of the agentswere 11,357 for Bob’s role and 10,655 for Alice’s role. In addition, only 66.5% of thenegotiations ended with a full agreement. We then ran their revised agents against themselves.
Consequently, significantly more negotiations ended with a full agreement (76.8%, p-value <0.02), resulting also in higher average utility values of 13,348 for Bob and 12,113 for Alice.
When the agents played the role of Bob these results were also significant ( p-value < 0.04).
We believe that if we had more student-designed agents the average utility values the agentsachieved could have been significantly better in both roles, both in the Class Project domain
and in the Britain–Zimbabwe domain.
In this set of experiments we also gave the students questionnaires to help qualitatively
assess the efficiency of the domain and agent repositories. The students had to rate several
statements on a scale of 1 (being the lowest) to 7 (being the highest). The students indicatedthat their agents were more generic after the second domain was introduced. The averagescore for the agent’s generality in the first stage was 5.38 compared to 6.08 for the revised
version. Overall, the students rated their agents’ generality as 6.0, and they asserted that
their agents would succeed in playing well in other domains as well, with an average ratingof 5.38.
5. THE FIRST ANACIn May 2010, we organized the first ANAC (see Baarslag et al. 2010) in conjunction with the Ninth International Conference on Autonomous Agents and Multiagent

GENIUS: SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORSTABLE 5. The Seven Participating Teams of ANAC 2010 from Five Different Universities.

IAMhaggler

University of Southampton

IAMcrazyHaggler
Agent K
NozomiFSEGAAgent Smith
Yushu

University of Southampton
Nagoya Institute of Technology
Nagoya Institute of Technology
Babes Bolyai University
TU Delft
University of Massachusetts Amherst

TABLE 6. The Three Domains Used at ANAC 2010.

Number of issues
Size
Opposition

Itex–Cypress

Zimbabwe–Britain

Travel4
180Strong

5
576
Medium7
188,160
Weak

Systems (AAMAS-10). In this section, we analyze the benefits of using GENIUS for thiscompetition.
Seven teams from five different universities participated in ANAC 2010, as listed
in Table 5. Each team had to design and build a negotiation agent using the GENIUS
framework.
We selected three domains and profiles on which the negotiating agents had to reach an
agreement. We aimed for a good spread of domain parameters, such as the number of issues,
the number of possible proposals, and the opposition of the domain (see Table 6).
In one scenario, taken from Kersten and Zhang (2003), a buyer–seller business negotiation is held between Itex Manufacturing, a producer of bicycle components and Cypress
Cycles, a builder of bicycles. The Zimbabwe–Britain domain (Lin et al. 2008) is the same weused for the experiments in which we tested the efficacy of GENIUS (see Section 4.1). Finally,a travel domain was used at ANAC 2010, whereby two friends negotiate on the location of
their next holiday. All of the domains were constructed using GENIUS environment.
We refer the reader to Baarslag et al. (2010) for more details on the tournament setup
and results of the negotiation competition. We now proceed with a description of the benefits
of GENIUS for this competition.
5.1. GENIUS—An Agent Development Tool
As we mentioned in Section 3, the GENIUS framework provides skeleton classes to facilitate the design of negotiating agents. Other aspects of negotiation—specifying information
about the domain and preferences, sending messages between the negotiators while obeying a specified negotiation protocol, declaring an agreement—is handled by the negotiation
environment. This allows the agent’s designer to focus on the implementation of the agent.
The agent’s designer only needs to implement an agent interface provided by the GENIUS
framework. In essence, the agent’s developer implements two methods: one for receiving a
proposal, and one for making a proposal. The rest of the interaction between the agents is
controlled by GENIUS.

COMPUTATIONAL INTELLIGENCETABLE 7. Highlighted Functionality of the API Available to the Agent to Access Information about the
Negotiation Environment and Its Preferences.

Agent

IssueDiscrete (implements Issue)

Action chooseAction()
Enables the agent to offer a bid to the opponent.
String getName()
Returns the name of the agent.
List<AgentParam> getParameters()
Accesses any parameters that are externallygiven to the agent.
Timeline getTimeline()
Gets information about possible time
constraints.
Double getUtility(Bid bid)
Computes the discounted utility of a bid, given
the current Timeline.
UtilitySpace getUtilitySpace()
Gets the preference profile of the agent.
receiveMessage(Action opponentAction)
Informs the agent about the opponent’s action.

String getDescription()
Returns a short description of the issue.
String getName()
Returns the name of the issue.
List<ValueDiscrete> getValues()
Returns all values associated with thisissue.

Double getTime()
Gets the normalized elapsed time in [0, 1].
long getTotalSeconds()
Gets the total negotiation time in seconds.

Bid

UtilitySpace

Value getValue(Issue issue)
Returns the selected value of a given issue inthe current bid.
setValue(Issue issue, Value value)
Sets the value of an issue.

Double getDiscountFactor()
Gets the discount factor.
Bid getMaxUtilityBid()
Computes the best possible bid given thepreferences of the agent.
Double getReservationValue()Gets the agent’s reservation value.
Double getUtility(Bid bid)
Computes the utility of a given bid.

Domain
List<Issue> getIssues()
Gets all issues of the negotiation domain.
long getNumberOfPossibleBids()
Returns the total amounts of bids the agents canmake.

Timeline
Double getElapsedSeconds()
Returns the seconds that have elapsed since
the start of the negotiation.

ValueDiscrete (implements Value)
String getValue()
Returns the text representation of this value.

GENIUS was freely available to the ANAC participants and researchers to develop and test
their agent. Table 7 gives an overview of the most important information that was available
to the agent through the API provided by GENIUS.
5.2. GENIUS—A Tournament Platform and Analysis Tool
The flexibility provided by the built-in general repository makes GENIUS an effective
tournament platform. The contestants of ANAC are able to upload their agent source code

GENIUS: SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORSFIGURE 9. A tournament session with the ANAC 2010 agents using the GENIUS interface.

(or even compiled code) to the ANAC organizers. The agents are then added to the GENIUS
repository. The ANAC agents and domains are bundled in the GENIUS repository and released
to the public after the tournament. GENIUS also provides a uniform, standardized negotiation
protocol and scoring system, as every developing team implements the agent inside the same
GENIUS environment.
GENIUS supports a number of different protocols, such as the alternating offers protocol,
one-to-many auctions, and many-to-many auctions. See Figure 8 for an overview of the typesof tournaments that can be run.
The analytical toolbox of GENIUS (see Figure 9) provides a method to evaluate the negotiation strategies employed by the ANAC participants. The toolbox gives valuable graphicalinformation during the negotiation sessions, including: Pareto optimal solutions, Nash product, Kalai–Smorodinsky. The negotiation log gives insight into the agent’s reasoning process
and can help improve the agent code. When a particular negotiation has finished, an entry
is added to the tournament overview, containing information about the number of rounds
used, and both utilities associated with the agreement that was reached by the agents. This
information can be used to assess the optimality of the agreements reached, either for both
agents, or for each agent individually. The result of the analysis can help new agent designers
to improve their agents as they play against previous ANAC strategies.
For example, when analyzing the tournament, we have observed that Agent K has won
by a relatively large margin, yet it only dominated the Travel domain. On Itex–Cypress andBritain–Zimbabwe domains, it earned second place after Nozomi and Yushu, respectively.
However, Agent K has won the competition due to its consistent high scores in all domains.
Most of the agents had problems on the Travel domain, the biggest domain of ANAC 2010.
With such a large domain it becomes unfeasible to enumerate all possible proposals. OnlyCOMPUTATIONAL INTELLIGENCE

Agent K, Nozomi, and IAM(crazy)Haggler were able to effectively negotiate with each otherin this domain, which resulted in less break-offs for them, hence their higher scores.
5.3. New Developments in GENIUS
At the end of ANAC 2010, the participating teams held a closing discussion. The
consensus among participants was that ANAC was a success, and that the basic structure of
the game should be retained. The discussion also yielded valuable suggestions for improving
the design of GENIUS for future ANAC competitions:(1) Domains with discount factors should be included in the tournament.
(2) Changes should be made to the deadline setup.
The generic setup of GENIUS makes it easy to extend it for use with new protocols andnegotiation rules. We released a new, public build of GENIUS4 containing all relevant aspectsof ANAC 2010. In particular, this includes all domains, preference profiles, and agents thatwere used in the competition, in addition to the proposed improvements that were decided
upon during the discussion. Consequently, the complete setup of ANAC is available to thenegotiation research community.
6. CONCLUSIONSThis paper presents a simulation environment that supports the design of generic automated negotiators. Extensive simulations with more than 60 computer science students
were conducted to validate the efficacy of the simulation environment. The results showthat GENIUS indeed supports the design of general automated negotiators, and even enables
the designers to improve their agents’ performance while retaining their generality. This isimportant as real-life negotiations are typically differentiated from one another. Furthermore,developing a good domain-dedicated strategy takes weeks and requires talent to do so.
We conducted experiments with automated agents in three distinct domains. The largest
domain comprised more than 15,000 possible agreements. Although this proves that thesimulation environment supports repositories of domains, we did not evaluate the agentson very large domains (e.g., more than 1,000,000 agreements). Many of the automatedagents the students designed took advantage of the small domains and reviewed all possible
agreements. This would be infeasible in larger domains with a deadline for the negotiationor each turn in the negotiation.
We conducted the first ANAC based on the GENIUS environment. GENIUS is freely
available for participants to develop and test their agent. Its easy to use agent skeleton makes
it a suitable platform for negotiating agent development. GENIUS has the ability to run a wide
range of different tournaments, an extensive repository of different agents and domains, and it
contains standardized protocols and a scoring system, thus making it the perfect tournament
platform for ANAC.
Finally, GENIUS has proved itself as a valuable and extendable research and analysis
tool for (post) tournament analysis. ANAC already yielded new state-of-the-art negotiation
strategies. Moreover, in light of the analysis of the results, we expect that next year even moresophisticated negotiation strategies will be developed. The second ANAC took place using
4 http://mmi.tudelft.nl/negotiation/index.php/Genius

GENIUS: SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORSa new GENIUS version in conjunction with the AAMAS conference in 2011. The success ofthe first ANAC underlines the importance of a flexible and versatile negotiation simulation
environment such as GENIUS.
The development of GENIUS was crucial to the organization of ANAC and conversely,
ANAC also has had an impact on the evolution of GENIUS. GENIUS is constantly being improved. We have released a new, public build of GENIUS that contains all relevant componentsof ANAC 2010, to make the complete setup of ANAC available to the negotiation researchcommunity. We also plan to continue and extend the capabilities of GENIUS, including theincorporation of nonlinear utility functions.
Future research will enable the use of GENIUS for the design of automated negotiatorsthat can successfully and efficiently negotiate with human negotiators. We observed thatsome of the students took advantage of the fact that they were aware that their agentswould be matched only with other automated agents. It would be interesting to evaluate theperformance of their agents against human negotiators as well.
We plan to run complete tournaments between the agents in the repository on all available
negotiation domains. This will allow us to identify the most efficient strategy currently
available in the repository. In addition, we believe that efficiency of a negotiation strategy
can depend on the opponent’s strategy as well as on the characteristics of the negotiation
domain and preference profiles. The analytical toolbox of GENIUS will allow us to identify
such dependencies and understand the reasoning behind them. Logs of negotiation sessions
produced by GENIUS can be used to discover patterns of negotiation behavior of the automatednegotiation strategies of human negotiators.
We also plan to use GENIUS as a training environment to teach people negotiation
concepts, such as exploration of outcome spaces, analysis of opponent’s offers, trade-offs
between issues, using concession tactics, etc. Another research direction includes the extension of GENIUS to enable argumentation and explanation, by allowing the agents to explain
their actions to people.

ACKNOWLEDGMENTS
This research is based upon work supported in part under NSF grant 0705587, by the U.S.
Army Research Laboratory and the U.S. Army Research Office under grant number W911NF08-1-0144 and by ERC grant #267523. Furthermore, this research is supported by the Dutch
Technology Foundation STW, applied science division of NWO, and the Technology Programof the Ministry of Economic Affairs. It is part of the Pocket Negotiator project with grant
number VIVI-project 08075.
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