Automated Negotiation

This paper proposes multi-agent e-negotiation support architecture for boundary conflict resolution. We use intelligent agents to act as human assistants during the negotiation task and provide means and resources for making final decisions in case there is a discrepancy in opinions about the decision matter. As an illustration we describe a scenario in marine weather forecasting and discuss how the proposed framework may support forecasters when negotiating agreements toward a consistent forecast policy at the boundary of regional forecasting centres.

We give an approach to automating the negotiation of business contracts. Our goal is to develop a language for both partially-specified contracts that are in the midst of being negotiated, including via automated auctions. Our starting point for this language is Courteous Logic Programs (CLP's), a form of logic-based knowledge representation (KR) that is semantically declarative, intuitively natural, computationally tractable, and practically executable. A CLP is suitable in particular to represent a fullyspecified executable contract. The basic CLP KR also facilitates modification during negotiation, because it includes prioritized confllct handling features that facilitate modification. Beyond the basic CLP KR, we have developed an initial ontology, and an associated style of representation, to specify additional aspects of a partial contract and of a negotiation process. The initial ontology specifies the set of negotiables and the structure of a contract in terms of its component goods/services and attributes. Specifying the negotiable aspects of a good or service includes specifying its attributes, their possible values, and dependencies/constraints on those attributes. Building upon the representation of these negotiable aspects, we are in current work developing methods to structure negotiations, especially to select and configure auction mechanisms to carry out the negotiation. This work brings together two strands of our previous work on business process automation in electronic commerce: representing business rules shared between enterprises, and conflgurable auction mechanisms.

Results from disaster research suggest that methods for coordination between individual emergency responders and organizations should recognize the independence and autonomy of these actors. These actor features are key factors in effective adaptation and improvisation of response to emergency situations which are inherently uncertain. Autonomy and adaptability are also well-known aspects of a multi-agent system (MAS). In this paper we present two MAS strategies that can effectively handle aircraft deicing incidents. These MAS strategies help improve to prevent and reduce e.g. airplane delays at deicing stations due to changing weather conditions or incidents at the station, where aircraft agents adopting pre-made plans that would act on behalf of aircraft pilots or companies, would only create havoc. Herein each agent using its own decision mechanism deliberates about the uncertainty in the problem domain and the preferences (or priorities) of the agents. Furthermore, taking both these issues into account each proposed MAS strategy outperforms a naive first-come, first-served coordination strategy. The simulation results help pilots and companies taking decisions with respect to the scheduling of the aircraft for deicing when unexpected incidents occur: they provide insights in the impacts and means for robust selection of incident-specific strategies on e.g. deicing station delays of (individual) aircraft.

Results from disaster research suggest that methods for coordination between individual emergency responders and organizations should recognize the independence and autonomy of these actors. These actor features are key factors in effective adaptation and improvisation of response to emergency situations which are inherently uncertain. Autonomy and adaptability are also well-known aspects of a multi-agent system (MAS). In this paper we present two MAS strategies that can effectively handle aircraft deicing incidents. These MAS strategies help improve to prevent and reduce e.g. airplane delays at deicing stations due to changing weather conditions or incidents at the station, where aircraft agents adopting pre-made plans that would act on behalf of aircraft pilots or companies, would only create havoc. Herein each agent using its own decision mechanism deliberates about the uncertainty in the problem domain and the preferences (or priorities) of the agents. Furthermore, taking both these issues into account each proposed MAS strategy outperforms a naive first-come, first-served coordination strategy. The simulation results help pilots and companies taking decisions with respect to the scheduling of the aircraft for deicing when unexpected incidents occur: they provide insights in the impacts and means for robust selection of incident-specific strategies on e.g. deicing station delays of (individual) aircraft.

While argumentation-based negotiation has been accepted as a promising alternative to game-theoretic or heuristic based negotiation, no evidence has been provided to confirm this theoretical advantage. We propose a model of bilateral negotiation extending a simple monotonic concession protocol by allowing the agents to exchange information about their underlying interests and possible alternatives to achieve them during the negotiation. We present an empirical study that demonstrates (through simulation) the advantages of this interest-based negotiation approach over the more classic monotonic concession approach to negotiation.

Argumentation has played a significant role in understanding and unifying under a common framework different forms of defeasible reasoning in Artificial Intelligence (AI). Argumentation is also close to the original inception of logic as a framework for formalizing human debate and dialogue. The purpose of this paper is to draw a formal connection between argumentation and classical reasoning, as supported by Propositional Logic. To this effect, we propose Argumentation Logic and show properties thereof.

The automated negotiation topic plays an important role in e-commerce research. However, despite considerable work on automated negotiation, few research efforts have aimed at software engineering facilities such a reuse and flexibility. To address this issue, we propose a novel computation environment for building agents with flexible negotiation strategies to function in various virtual business domains. Regarding the negotiation strategies, some decision taking assistance techniques may be used in group, e.g. rule-based reasoning and techniques to machine learning. Considering the flexibility of acting in various business domains, a specific agent can be programmed to work in as many business domains as necessary, and essentially, it will also be possible to reconfigure the agent´s business domains at execution time. All the experiments in this work were idealized in the terms of a tourism negotiation package. Results from these experiments demonstrate effectiveness of our proposal.

The automated negotiation topic plays an important role in e-commerce research. However, despite considerable work on automated negotiation, few research efforts have aimed at software engineering facilities such a reuse and flexibility. To address this issue, we propose a novel computation environment for building agents with flexible negotiation strategies to function in various virtual business domains. Regarding the negotiation strategies, some decision taking assistance techniques may be used in group, e.g. rule-based reasoning and techniques to machine learning. Considering the flexibility of acting in various business domains, a specific agent can be programmed to work in as many business domains as necessary, and essentially, it will also be possible to reconfigure the agent´s business domains at execution time. All the experiments in this work were idealized in the terms of a tourism negotiation package. Results from these experiments demonstrate effectiveness of our proposal.

The automated negotiation topic plays an important role in e-commerce research. However, despite considerable work on automated negotiation, few research efforts have aimed at software engineering facilities such a reuse and flexibility. To address this issue, we propose a novel computation environment for building agents with flexible negotiation strategies to function in various virtual business domains. Regarding the negotiation strategies, some decision taking assistance techniques may be used in group, e.g. rule-based reasoning and techniques to machine learning. Considering the flexibility of acting in various business domains, a specific agent can be programmed to work in as many business domains as necessary, and essentially, it will also be possible to reconfigure the agent´s business domains at execution time. All the experiments in this work were idealized in the terms of a tourism negotiation package. Results from these experiments demonstrate effectiveness of our proposal.

Humans when evaluating possible options, often reduce the possibilities that have to be evaluated at each step by eliminating those that did not satisfy the previous criteria. We will show that when agents use this approach their utility space becomes less complex. As result, a negotiation mechanism can support more number of agents. Moreover we will show that negotiations between agents that use this approach have more chance of locating a deal. We call this approach the subset rule. Specically we consider modeling negotiations where many agents have to agree upon one option among available many. We assume that the negotiation matter is described by multiple interdependent issues. Each issue can have multiple possible values. The issues are interdependent means that it is not possible simplify the negotiation by negotiating over one issue at a time. When this is the case, the contract space is usually large and we face two challenges. The rst is the diculty of evaluating each possi...

Negotiations in which participants exchange offers based on their chosen positions can be extended to include dialogue about their interests. Revelation of negotiators' interests allows them to make more acceptable offers and perhaps propose possible alternative approaches toward each other's interests, both of which may result in mutually and individually beneficial outcomes. However, it can also expose their strategies, and possibly their dependencies on other negotiators toward the achievement of their goals. Revealing this information can leave them vulnerable to extortion or retribution, but it can also be used to gain sympathy or build a relationship of trust and reciprocity.

Today D2H services have become an integral part of our everyday life. Countries across the world have announced for their citizens to compulsorily adopt it which has drastically reduced the cable TV system..There is cutthroat competition in the market for D2H service providers. Today when customer satisfaction is key to the success of your business, technology which can help in enhancing the quality of D2H services provided to the customer and can provide better and more interactive interface to the customer will definitely boost up the business of any service provider. Web semantics is such an emerging web services technology which can really bring about a drastic improvement in D2H services experience of the customer as well the service provider. With the increase in the number of services it is highly complex to manage them, being able to understand the customer preferences. Whereas much of the work in Semantic web Services discovery concentrated on the functionalities of the services, contextual information, personal preferences and more generally personalisation are more challenging areas in the Semantic computing arena. Today understanding customer preferences and providing her the preferred services anytime anywhere is the mantra to success for any Service provider. To illustrate our requirements , in this position we cite one of several use cases that is based on some earlier work in the field of Web Services selection which aims at leveraging Semantic web services for D2H applications. The use cases are based on our work on D2HOnt-a Semantic toolbox to explore D2H user centred services on the semantic web. Our vision is to take full advantage of future complex service offerings on limited client devices and to handle the need for personalised service discovery in D2H environments.

Location-based Access Control (LBAC) techniques allow the definition of users' access rights based on location predicates that exploit the users' physical location. However, evaluating the physical location of a user is a specialized activity that is unlikely to be performed by the same entity (e.g., organization or system) in charge of the access control decision. For this reason, location evaluation is usually assumed to be provided by specific Location Services (LSs) possibly coexisting in a same area and competing one with the others. In this paper, we address the issues related to the communication and negotiation between an Access Control Engine (ACE) enforcing access rules that include location-based predicates and multiple, functionally equivalent, LSs. We introduce metadata for the exchange of service level agreement attributes between the ACE and the LSs. Based on such metadata we develop different negotiation protocols, from a basic negotiation protocol that shows the core aspects of our proposal to an enhanced protocol that enriches the interaction by taking into account a cost/benefit analysis and some service requirements. Finally, we present an extension to the enhanced protocol to consider possible time validity constraints on access control decisions.

En observaciones propias mediante salidas al campo, capturas con trampas luminosas y de feromonas hemos comprobado el aumento persistente de individuos adultos del lepidoptero Spodoptera exigua , oruga gardama o verde, que nos ha hecho pensar en la posibilidad de que los ataques y danos puedan tener mayor importancia economica de la conocida...

Software agents have the potential to serve as effective surrogates of humans in conducting business transactions in an electronic commerce environment. The reported proceeding research aims to evaluate the performance of software agents in automated transaction negotiations. As part of this research, agents are being built using IBM aglets, and their performance evaluation within various experimental settings is currently underway. Research Overview and Motivation The proliferation of newly emerging Internet and WWW technologies has created innovative and exciting multidisciplinary research opportunities in information systems/technology. Software agent is one research area that has received fast-growing interests and attention from researchers and practitioners alike. In an era characterized by new business models established and prevalent in electronic marketspace, examination of software agents for automated transaction negotiations is essential and timely.

Mechanism design provides a framework to solve distributed optimization problems in systems of self-interested agents. The combinatorial auction is one such problem, in which there is a set of discrete items to allocate to agents. Unfortunately, recent results suggest that it is impossible to implement reasonable approximations without losing robustness to manipulation. Furthermore, the Vickrey-Clarke-Groves (VCG) mechanism is known to be vulnerable to manipulation when agents can bid under multiple false names. In this paper we relax incentive constraints and require only that useful manipulation be NP-hard. We prove that any tractable approximation algorithm can be made to produce a hard-to-manipulate (VCG-based) mechanism, providing a useful counterpoint to these negative results. We also show that falsename bid manipulation in the VCG is NP-hard.Engineering and Applied Science

This article reviews recent research and current art in the area of auction based mechanisms for electronic procurement. These mechanisms are becoming increasingly relevant in modern day e-procurement systems since they enable a promising way of automating negotiations with suppliers and achieving the ideal goals of procurement efficiency, cost minimization, and agent based deployment. The survey delineates different representative scenarios in e-procurement where auctions can be deployed and describes the conceptual and mathematical aspects of different categories of procurement auctions. We discuss three categories: (1) multi-unit auctions for a single homogeneous type of item; (2) combinatorial procurement auctions where the buyer seeks to procure a bundle of multiple items and the suppliers bid for subsets of the bundle; and (3) multi-attribute auctions where the procurement decisions transcend cost considerations alone, to take into account lead times, logistics costs, and other important attributes. In all three cases, the winner determination problem and the determination of payments turn out to be interesting and challenging combinatorial optimization problems. In our review, we present mathematical formulation of procurement scenarios under each category, bring out the challenge involved in solving the problems, and indicate active research topics. We also present a case study of electronic procurement at General Motors. A Note to Practitioners-Since the burst of the dot.com bubble, many procurement professionals and purchasing managers have begun to question the ability of the Internet to redefine procurement processes within their firms. In this article we set out to show that this would be a misplaced sense of deja vu because the Internet along with a milieu of decision technologies based on Game Theory and Optimization is proving to be a significant tool in the hands of procurement professionals. Sans all the hype, the dot.com phenomena has left behind useful ideas including that of e-platforms for on-line auctions. Building upon this core conceptual construct, familiar to most procurement professionals, we first illustrate the successful implementation of sophisticated auction models by pioneering firms, based on optimization technologies, that meet the requirements of complex business-to-business procurement. We then discuss the exciting field of research this has opened up with a vast potential for immediate and gainful applications. We review the existing stateof-the-art in this field, track its recent developments and classify the models available for different procurement scenarios. We also provide pointers to areas that require further fundamental as well as applied research which calls for the attention of not just academic researchers but also practicing professionals.

Auctions provide an e cient w ay of resolving one-to-many negotiations. This is particularly true for automated agents where delays and long communications carry negative e x t e rnalities. A properly designed auction, tailored to the speci c needs of the relevant m ulti-agent system, can signi cantly improve its performance. In this paper we focus on the speci c problem of service allocation amongst autonomous, automated agents, within the context of the ADEPT project which concerns the BT (British Telecom) business process of providing a quote for designing a network for a customer. The main contributions of this paper are threefold: First, we s h o w h o w an English auction can be modi ed for services, which a r e m ulti-dimensional private value objects. Second, we s h o w h o w, under certain conditions, auctions can be arranged by the service providing agents, in the cases where the service seeking agents fail to do so. We consider the incentives of all participants, and show h o w s u c h an arrangement c a n be in their best interest. Finally, by examining our results for what is, essentially, an application of gametheory and mechanism design to an existing application, we draw some general conclusions on how s u c h concepts can be operationalized in automated agents.

With existing technology, it is already possible for personal agents to schedule meetings for their users, to write the small print of an agreement, and for agents to search the Internet for the cheapest price. But serious negotiation cranks the difficulty of the problem up several notches. In this paper, we review what game theory has to offer in the

Auctions provide an e cient w ay of resolving one-to-many negotiations. This is particularly true for automated agents where delays and long communications carry negative e x t e rnalities. A properly designed auction, tailored to the speci c needs of the relevant m ulti-agent system, can signi cantly improve its performance. In this paper we focus on the speci c problem of service allocation amongst autonomous, automated agents, within the context of the ADEPT project which concerns the BT (British Telecom) business process of providing a quote for designing a network for a customer. The main contributions of this paper are threefold: First, we s h o w h o w an English auction can be modi ed for services, which a r e m ulti-dimensional private value objects. Second, we s h o w h o w, under certain conditions, auctions can be arranged by the service providing agents, in the cases where the service seeking agents fail to do so. We consider the incentives of all participants, and show h o w s u c h an arrangement c a n be in their best interest. Finally, by examining our results for what is, essentially, an application of gametheory and mechanism design to an existing application, we draw some general conclusions on how s u c h concepts can be operationalized in automated agents.

Negotiations are one of the most common ways that agents in a multi-agent system use to reach agreements. As negotiations commonly are multilateral and multi-issue, these processes become more difficult. Moreover, in real-world applications in which real-time agents are needed, this issue becomes more important. Autonomous agents should be able to decide what to propose in each round of negotiations quickly. In this situation if an agent is able to predict opponent's behavior including its next offer, the task of offering comes to be more efficient. This paper presents an approach in which an agent can predict opponent's next offer using a history of previous offers and counter-offers by the aid of ARTMAP Neural Network. The agent can employ this information to determine its offer after a what-if analysis of possible offers. The experimental results show that this approach substantially decreases the duration of negotiations and can be used in real applications as well.

CP-nets have proven to be an effective representation for capturing preferences. However, their use in automated negotiation is not straightforward because, typically, preferences in CP-nets are partially ordered and negotiating agents are required to compare any two outcomes based on a request and an offer in order to negotiate effectively. If agents know how to generate total orders from their CP-nets, they can make this comparison. This paper proposes heuristics that enable the use of CP-nets in utility-based negotiations by generating total orderings. To validate this approach, the paper compares the performance of CP-nets with our heuristics with the performance of UCP-nets that are equipped with complete preference orderings. Our results show that we can achieve comparable performance in terms of the outcome utility. More importantly, one of our proposed heuristics can achieve this performance with significantly smaller number of interactions compared to UCP-nets.

Automated negotiation is important for carrying out flexible transactions. Agents that take part in automated negotiation need to have a concise representation of their user's preferences and should be able to reason on these preferences effectively. We develop an automated negotiation platform wherein consumer agents negotiate with producer agents about services. A consumer agent represents its user's preferences in a compact way using a CP-net, which is a structure that allows users to order their preferences based on the different value combinations of attributes. Acquiring user's preferences in a compact way is crucial since it significantly decreases the number of questions to be asked to the user by the consumer agent. We design strategies for consumer agents to reason on and negotiate effectively with the preference graph induced from a CP-net. These strategies are designed to generate deals that are acceptable by the provider and the consumer. We compare our proposed strategies in terms of how well and how quickly they can find desirable deals for the consumer.

In online and dynamic e-commerce environments, it is beneficial for parties to consider each other's preferences in carrying out transactions. This is especially important when parties are negotiating, since considering preferences will lead to faster closing of deals. However, in general may not be possible to know other participants' preferences. Thus, learning others' preferences from the bids exchanged during the negotiation becomes an important task. To achieve this, the producer agent may need to make assumptions about the consumer's preferences and even its negotiation strategy. Nevertheless, these assumptions may become inconsistent with a variety of preference representations. Therefore, it is more desired to develop a learning algorithm, which is independent from the participants' preference representations and negotiation strategies. This study presents a negotiation framework in which the producer agent learns an approximate model of the consumer's preferences regardless of the consumer's preference representation. For this purpose, we study our previously proposed inductive learning algorithm, namely Revisable Candidate Elimination Algorithm (RCEA). Our experimental results show that a producer agent can learn the consumer's preferences via RCEA when the consumer represents its preferences using constraints or CP-nets. Further, in both cases, learning speeds up the negotiation considerably.

In online, dynamic environments, the services requested by consumers may not be readily served by the providers. This requires the service consumers and providers to negotiate their service needs and offers. Multiagent negotiation approaches typically assume that the parties agree on service content and focus on finding a consensus on service price. In contrast, this work develops an approach through which the parties can negotiate the content of a service. This calls for a negotiation approach in which the parties can understand the semantics of their requests and offers and learn each other's preferences incrementally over time. Accordingly, we propose an architecture in which both consumers and producers use a shared ontology to negotiate a service. Through repetitive interactions, the provider learns consumers' needs accurately and can make better targeted offers. To enable fast and accurate learning of preferences, we develop an extension to Version Space and compare it with existing learning techniques. We further develop a metric for measuring semantic similarity between services and compare the performance of our approach using different similarity metrics.

We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user's preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user's preferences, but also its opponent's preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer's needs and to offer services that respect consumer's preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the consumer's preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer's preferences are specified in complex ways, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.

In multi-issue negotiations, autonomous agents can act co- operatively to benefit from mutually preferred agreements. However, empirical evidence suggests that they often fail to search for joint gains and end up with inefficient results. To address this problem, this paper proposes a novel mediated negotiation procedure to support the negotiation agents in reaching an efficient and fair agreement in bilateral

This paper presents an experiment performed to test the properties of an innovative bargaining mechanism (called automated negotiation) used to resolve disputes arising from Internet-based transactions. The main result shows that the settlement rule tends to chill bargaining as it creates incentives for individuals to misrepresent their true valuations, which implies that automated negotiation is not able to promote agreements. However, this perverse effect depends strongly on the conflict situation. When the threat that a disagreement occurs is more credible, the strategic effect is reduced since defendants are more interested in maximizing the efficiency of a settlement than their own expected profit. The implications of these results are then used to discuss the potential role of public regulation and reputation mechanisms in Cyberspace.

Automated negotiation by software agents is a key enabling technology for agent mediated e-commerce. To this end, this paper considers an important class of such negotiationsnamely those in which an agent engages in multiple concurrent bilateral negotiations for a good or service. In particular, we consider the situation in which a buyer agent is looking for a single service provider from a number of available ones in its environment. By bargaining simultaneously with these providers and interleaving partial agreements that it makes with them, a buyer can reach good deals in an efficient manner. However, a key problem in such encounters is managing commitments since an agent may want to make intermediate deals (so that it has a definite agreement) with other agents before it gets to finalize a deal at the end of the encounter. To do this effectively, however, the agents need to have a flexible model of commitments that they can reason about in order to determine when to commit and to decommit. This paper provides and evaluates such a commitment manager and integrates it into the negotiation model. * The first author is a PhD student who is funded by British Telecom (BT). 1 This view is now widespread in a range of domains that we are targeting for our work, including the web, the grid, pervasive computing and e-business.

Coalition formation is a key problem in automated negotiation among self-interested agents, and other multiagent applications. A coalition of agents can sometimes accomplish things that the individual agents cannot, or can do things more efficiently. However, motivating the agents to abide to a solution requires careful analysis: only some of the solutions are stable in the sense that no group of agents is motivated to break off and form a new coalition. This constraint has been studied extensively in cooperative game theory. However, the computational questions around this constraint have received less attention. When it comes to coalition formation among software agents (that represent real-world parties), these questions become increasingly explicit. In this paper we define a concise general representation for games in characteristic form that relies on superadditivity, and show that it allows for efficient checking of whether a given outcome is in the core. We then show that determining whether the core is nonempty is N P-complete both with and without transferable utility. We demonstrate that what makes the problem hard in both cases is determining the collaborative possibilities (the set of outcomes possible for the grand coalition), by showing that if these are given, the problem becomes tractable in both cases. However, we then demonstrate that for a hybrid version of the problem, where utility transfer is possible only within the grand coalition, the problem remains N Pcomplete even when the collaborative possibilities are given.

This paper presents an automated peer-to-peer negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative considering heterogeneity prosumer preferences. The heterogeneity arises from prosumers' evaluation of energy contracts through multiple societal and environmental criteria and the prosumers' private preferences over those criteria. The prosumers engage in bilateral negotiations with peers to mutually agree on periodical energy contracts/loans consisting of the energy volume to be exchanged at that period and the return time of the exchanged energy. The negotiating prosumers navigate through a common negotiation domain consisting of potential energy contracts and evaluate those contracts from their valuations on the entailed criteria against a utility function that is robust against generation and demand uncertainty. From the repeated interactions, a prosumer gradually learns about the compatibility of its peers in reaching energy contracts that are closer to Nash solutions. Empirical evaluation on real demand, generation and storage profiles-in multiple system scales-illustrates that the proposed negotiation based strategy can increase the system efficiency (measured by utilitarian social welfare) and fairness (measured by Nash social welfare) over a baseline strategy and an individual flexibility control strategy representing the status quo strategy. We thus elicit system benefits from peer-to-peer flexibility exchange already without any central coordination and market operator, providing a simple yet flexible and effective paradigm that complements existing markets.

Many day-today tasks require negotiation, mostly under conditions of incomplete information. In particular, the opponent's exact tradeoff between different offers is usually unknown. We propose a model of an automated negotiation agent capable of negotiating with a bounded rational agent (and in particular, against humans) under conditions of incomplete information. Although we test our agent in one specific domain, the agent's architecture is generic; thus it can be adapted to any domain as long as the negotiators' preferences can be expressed in additive utilities. Our results indicate that the agent played significantly better, including reaching a higher proportion of agreements, than human counterparts when playing one of the sides, while when playing the other side there was no significant difference between the results of the agent and the human players.

The development of proficient automated agents has flourished in recent years, yet making the agents interact with people has still received little attention. This is mainly due to the unpredictable nature of people and their negotiation behavior, though complexity and costs attached to experimentation with people, starting from the design and ending with the evaluation process, is also a factor. Even so, succeeding in designing proficient automated agents remains an important objective. In recent years we have invested much effort in facilitating the design and evaluation of automated agents interacting with people, making it more accessible to researchers. We have created two distinct environments for bargaining agents, as well as proposing a novel approach for evaluating agents. These are key factors for making automated agents become a reality rather than remain theoretical.

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Negotiation surrounds our day-today lives. Research in the field of automated negotiations has suggested the design and use of automated negotiators, on one hand to allow facilitation of the negotiation process by human negotiators and, on the other hand to provide automated agents that can negotiate on behalf of humans. Many papers present innovative agents and evaluate their efficacy in negotiations with other automated agents or people. Others focus on building negotiation support systems with the purpose of helping negotiators reach an agreement. Yet, the question still remains whether these systems or agents have the potential of improving people's negotiation skills. In this paper we attempt to shed more light on this topic. By means of extensive simulations with human negotiators we examine and compare several training methods and their implications on the improvement of negotiation skills of human negotiators.

Research on automated negotiators has flourished in recent years. Among the important issues considered is how these automated negotiators can proficiently negotiate with people. To validate this, many experimentations with people are required. Nonetheless, conducting experiments with people is timely and costly, making the evaluation of these automated negotiators a very difficult process. Moreover, each revision of the agent's strategies requires to gather an additional set of people for the experiments. In this paper we investigate the use of Peer Designed Agents (PDAs)-computer agents developed by human subjects-as a method for evaluating automated negotiators. We have examined the negotiation results and its dynamics in extensive simulations with more than 300 human negotiators and more than 50 PDAs in two distinct negotiation environments. Results show that computer agents perform better than PDAs in the same negotiation contexts in which they perform better than people, and that on average, they exhibit the same measure of generosity towards their negotiation partners. Thus, we found that using the method of peer designed negotiators embodies the promise of relieving some of the need for people when evaluating automated negotiators.

Modern networked business environment enables design of flexible and effective mechanisms of exchange between economic parties. Online negotiations allow geographically and temporally separated participants to engage in exchange of offers in search for acceptable agreements. The digital medium enables development of software agents, which can assist with negotiation tasks while saving time and human effort. The current paper investigates the prospects of utilizing software agents in negotiations with the human counterparts. It presents the findings from experiment where human subjects acted as buyers negotiating with software agent sellers over a mobile phone plan. An electronic negotiation system incorporating software agents was used in the experiment. The agents employed various concession-making schedules while engaging in negotiation tasks involving one of two complexity levels. Negotiation task complexity was manipulated using different number of issues involved in the negotiations. Subjects were recruited among university students. Negotiations between the subjects and agents took place during a two-day period in an asynchronous mode through the web. The findings suggest that interaction between negotiation task complexity and negotiation tactic has significant effects on negotiation outcomes and subjective assessments by the human participants. In particular, task complexity had a higher impact on the agreement rate when agents employed a competitive tactic vs. when they used a conceding one.

In software engineering community there is an increasing effort of design and development of multiagent systems (MAS). Among several issues emerging from this initiative, complexity, quality and uncertainty issues have not yet received much attention. The main factors affecting quality of MAS are complexity and knowledgeability. Complexity of MAS can be defined in terms of structural and algorithmic complexity in either objective or subjective way. Knowledgeability of MAS can be defined in terms of problem solving and cognitive capabilities and the ability to cope with interactions, such as cooperation, coordination and competition. In this paper we define and address the quality and complexity issues of MAS and define metrics to measure the quality and complexity. The metrics are used for devising a candidate set of agents for MAS design. Furthermore, we address the knowledgeability of MAS and devise models and techniques to cope with uncertainty in competitive situations.

The automated negotiation topic plays an important role in e-commerce research. However, despite considerable work on automated negotiation, few research efforts have aimed at software engineering facilities such a reuse and flexibility. To address this issue, we propose a novel computation environment for building agents with flexible negotiation strategies to function in various virtual business domains. Regarding the negotiation strategies, some decision taking assistance techniques may be used in group, e.g. rule-based reasoning and techniques to machine learning. Considering the flexibility of acting in various business domains, a specific agent can be programmed to work in as many business domains as necessary, and essentially, it will also be possible to reconfigure the agent´s business domains at execution time. All the experiments in this work were idealized in the terms of a tourism negotiation package. Results from these experiments demonstrate effectiveness of our proposal.

Threats are used in persuasive negotiation dialogues when a proponent agent tries to persuade an opponent of him to accept a proposal. Depending on the information the proponent has modeled about his opponent(s), he may generate more than one threat, in which case he has to evaluate them in order to select the most adequate to be sent. One way to evaluate the generated threats is by calculating their strengths, i.e., the persuasive force of each threat. Related work considers mainly two criteria to do such evaluation: the certainty level of the beliefs that compose the threat and the importance of the goal of the opponent. This article aims to study the components of threats and propose further criteria that lead to improve their evaluation and to select more effective threats during the dialogue. Thus, the contribution of this paper is a model for calculating the strength of threats that is mainly based on the status of the goal of the opponent and the credibility of the proponent. The model is empirically evaluated and the results demonstrate that the proposed model is more efficient than previous works in terms of the number of exchanged arguments, and the number of reached agreements.

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Dans les entreprises, l'écart entre la date où les entreprises engagent leurs dépenses et la date où elles sont payées entraîne un besoin en fonds de roulement à l'origine de problèmes dont la sévérité va de la perte de contrat au dépôt de bilan. Dans le contexte actuel, le recours à l'emprunt pour éviter ces situations est hélas complexe pour les PME, les banques refusant de prêter lorsqu'elles estiment que le risque est trop important. Une possibilité est alors de recourir au factoring, et de faire financer ses créances. Dans cette thèse, nous proposons une place de marché de factoring intelligente, basée sur des tra vaux de négociation automatique et de mechanism design : nous proposons d'analyser théoriquement et expérimentalement les problématiques liées aux agents curieux cherchant à inférer les informations privées des autres agents, entraînant des surcoûts de leur point vue comme de celui de la plate-forme. Nous proposons également de concevoir un protocole de négociation qui s'oppose à de tels comporte ments, donnant aux agents une incitation à ne négocier que lorsqu'ils souhaitent vraiment obtenir un bien. Nous proposons ensuite un agent de négociation automatique. Pour la conception de la stratégie de notre agent, nous proposons d'exploiter les méthodes de Monte Carlo Tree Search, qui ont récemment fait leur preuve dans l'IA pour les jeux. Notre agent est capable d'apprendre de son partenaire bien que ce ne soit pas son objectif premier, afin de trouver un accord qui lui soit favorable. Il s'appuie pour cela sur des technique de modélisation d'adversaire impliquant des méthodes d'apprentissage tels que l'apprentissage bayésien et la régression de processus gaussiens.

This paper introduces a new Negotiating Agent for automated negotiation on continuous domains and without considering a specified deadline. The agent bidding strategy relies on Monte Carlo Tree Search, which is a trendy method since it has been used with success on games with high branching factor such as Go. It uses two opponent modeling techniques for its bidding strategy and its utility: Gaussian process regression and Bayesian learning. Evaluation is done by confronting the existing agents that are able to negotiate in such context: Random Walker, Tit-fortat and Nice Tit-for-Tat. None of those agents succeeds in beating our agent; moreover the modular and adaptive nature of our approach is a huge advantage when it comes to optimize it in specific applicative contexts.