An Evolutionary Approach for Learning Opponent's Deadline and Reserve Points in Multi-Issue Negotiation

The efficiency of automated multi-issue negotiation depends on the available information about the opponent. In a competitive negotiation environment, agents do not reveal their parameters to their opponents in order to avoid exploitation. Several researchers have argued that an agent's optimal strategy can be determined using the opponent's deadline and reserve points. In this paper, we propose a new learning agent, so-called Evolutionary Learning Agent (ELA), able to estimate its opponent's deadline and reserve points in bilateral multi-issue negotiation based on opponent's counter-offers (without any additional extra information). ELA reduces the learning problem to a system of non-linear equations and uses an evolutionary algorithm based on the elitism aspect to solve it. Experimental study shows that our learning agent outperforms others agents by improving its outcome in term of average and joint utility

Autonomous agents in bargaining games : an evolutionary investigation of fundamentals, strategies, and business applications

Bargaining is becoming increasingly important due to developments within the field of electronic commerce, especially the development of autonomous software agents. Software agents are programs which, given instructions from a user, are capable of autonomously and intelligently realise a given task. By means of such agents, the bargaining process can be automated, allowing products and services together with related conditions, such as warranty and delivery time, to be flexible and tuned to the individual preferences of the people concerned. In this theses we concentrate on both fundamental aspects of bargaining as well as business-related applications of automated bargaining using software agents. The fundamental part investigates bargaining outcomes within a stylised world, and the factors that influence these outcomes. This can provide insights for the production of software agents, strategies, and setting up bargaining rules for practical situations. We study these aspects using computational simulations of bargaining agents. Hereby we consider adaptive systems, i.e., where agents learn to adjust their bargaining strategy given past experience. This learning behaviour is simulated using evolutionary algorithms. These algorithms originate from the field of artificial intelligence, and are inspired by the biological theory of evolution. Originally, evolutionary algorithms were designed for solving optimisation problems, but they are now increasingly being used within economics for modelling human learning behaviour. Besides computational simulations, we also consider mathematical solutions from game theory for relatively simple cases. Game theory is mainly concerned with the “rational man”, that is, with optimal outcomes within an stylised setting (or game) where people act rationally. We use the game-theoretic outcomes to validate the computational experiments. The advantage of computer simulations is that less strict assumptions are necessary, and that more complex interactions that are closer to real-world settings can be investigated. First of all, we study a bargaining setting where two players exchange offers and counter offers, the so-called alternating-offers game. This game is frequently used for modelling bargaining about for instance the price of a product or service. It is also important, however, to allow other product- and service-related aspects to be negotiated, such as quality, delivery time, and warranty. This enables compromises by conceding on less important issues and demanding a higher value for relatively important aspects. This way, bargaining is less competitive and the resulting outcome can be mutually beneficial. Therefore, we investigate using computational simulations an extended version of the alternating-offers game, where multiple aspects are negotiated concurrently. Moreover, we apply game theory to validate the results of the computational experiments. The simulation shows that learning agents are capable of quickly finding optimal compromises, also called Pareto-efficient outcomes. In addition, we study the effects of time pressure that arise if negotiations are broken off with a small probability, for example due to external eventualities. In absence of time pressure and a maximum number of negotiation rounds, outcomes are very unbalanced: the player that has the opportunity to make a final offer proposes a take-it-or-leave-it offer in the last round, which leaves the other player with a deal that is only slightly better than no deal at all. With relatively high time pressure, on the other hand, the first offer is most important and almost all agreements are reached in the first round. Another interesting result is that the simulation outcomes after a long period of learning in general coincide with the results from game theory, in spite of the fact that the learning agents are not “rational”. In reality, not only the final outcome is important, but also other factors play a role, such as the fairness of an offer. Using the simulation we study the influence of such fairness norms on the bargaining outcomes. The fairness norms result in much more balanced outcomes, even with no time pressure, and seem to be closer outcomes in the real world. Negotiations are rarely isolated, but can also be influenced by external factors such as additional bargaining opportunities. We therefore also consider bargaining within a market-like setting, where both buyers and sellers can bargain with several opponents before reaching an agreement. The negotiations are executed consecutively until an agreement is reached or no more opportunities are available. Each bargaining game is reduced to a single round, where player 1 makes an offer and player 2 can only respond by rejecting or accepting this offer. Using an evolutionary simulation we study several properties of this market game. It appears that the outcomes depend on the information that is available to the players. If players are informed about the bargaining opportunities of their opponents, the first player in turn has the advantage and always proposes a take-it-or-leave-it deal that leaves the other player with a relatively poor outcome. This outcome is consistent with a game-theoretic analysis which we also present in this thesis. If this information is not available, a theoretical analysis is very hard. The evolutionary simulation, however, shows that in this case the responder obtains a better deal. This occurs because the first player can no longer anticipate the response of the other player, and therefore bids lower to avoid a disagreement. In this thesis, we additionally consider other factors that influence the outcomes of the market game, such as negotiation over multiple issues simultaneously, search costs, and break off probabilities. Besides fundamental issues, this thesis presents a number of business-related applications of automated bargaining, as well as generic bargaining strategies for agents that can be employed in related areas. As a first application, we introduce a framework where negotiation is used for recommending shops to customers, for example on a web page of an electronic shopping mall. Through a market-driven auction a relevant selection of shops is determined in a distributed fashion. This is achieved by selling a limited number of banner spaces in an electronic auction. For each arriving customer on the web page, shops can automatically place bids for this “customer attention space” through their shop agents. These software agents bid based on a customer profile, containing personal data of the customer, such as age, interests, and/or keywords in a search query. The shop agents are adaptive and learn, given feedback from the customers, which profiles to target and how much to bid in the auction. The highest bidders are then selected and displayed to the customer. The feasibility of this distributed approach for matching shops to customers is demonstrated using an evolutionary simulation. Several customer models and auction mechanisms are studied, and we show that the market-based approach results in a proper selection of shops for the customers. Bargaining can be especially beneficial if not only the price, but other aspects are considered as well. This allows for example to customise products and services to the personal preferences of a user. We developed a system makes use of these properties for selling and personalising so-called information goods, such as news articles, software, and music. Using the alternating-offers protocol, a seller agent negotiates with several buyers simultaneously about a fixed price, a per-item price, and the quality of a bundle of information goods. The system is capable of taking into account important business-related conditions such as the fairness of the negotiation. The agents combine a search strategy and a concession strategy to generate offers in the negotiations. The concession strategy determines the amount the agent will concede each round, whereas the search strategy takes care of the personalisation of the offer. We introduce two search strategies in this thesis, and show through computer experiments that the use of these strategies by a buyer and seller agent, result in personalised outcomes, also when combined with various concession strategies. The search strategies presented here can be easily applied to other domains where personalisation is important. In addition, we also developed concession strategies for the seller agent that can be used in settings where a single seller agent bargains with several buyer agents simultaneously. Even if bargaining itself is bilateral (i.e., between two parties), a seller agent can actually benefit from the fact that several such negotiations occur concurrently. The developed strategies are focussed on domains where supply is flexible and can be adjusted to meet demand, like for information goods. We study fixed strategies, time-dependent strategies and introduce several auction-inspired strategies. Auctions are often used when one party negotiates with several opponents simultaneously. Although the latter strategies benefit from the advantages of auctions, the actual negotiation remains bilateral and consists of exchanging offers and counter offers. We developed an evolutionary simulation environment to evaluate the seller agent’s strategies. We especially consider the case where buyers are time-impatient and under pressure to reach agreements early. The simulations show that the auction-inspired strategies are able to obtain almost maximum profits from the negotiations, given sufficient time pressure of the buyers

Evaluating Negotiation Behavior and Results: Can We Identify What We Say We Know?

This article was presented at the Columbus Community Legal Services Anniversary Symposium on Clinical Legal Education at the Catholic University of America, October 198

Evaluating Negotiation Behavior and Results: Can We Identify What We Say We Know?

This article was presented at the Columbus Community Legal Services Anniversary Symposium on Clinical Legal Education at the Catholic University of America, October 198

Automated Service Negotiation Between Autonomous Computational Agents

PhDMulti-agent systems are a new computational approach for solving real world, dynamic and open system problems. Problems are conceptualized as a collection of decentralised autonomous agents that collaborate to reach the overall solution. Because of the agents autonomy, their limited rationality, and the distributed nature of most real world problems, the key issue in multi-agent system research is how to model interactions between agents. Negotiation models have emerged as suitable candidates to solve this interaction problem due to their decentralised nature, emphasis on mutual selection of an action, and the prevalence of negotiation in real social systems. The central problem addressed in this thesis is the design and engineering of a negotiation model for autonomous agents for sharing tasks and/or resources. To solve this problem a negotiation protocol and a set of deliberation mechanisms are presented which together coordinate the actions of a multiple agent system. In more detail, the negotiation protocol constrains the action selection problem solving of the agents through the use of normative rules of interaction. These rules temporally order, according to the agents' roles, communication utterances by specifying both who can say what, as well as when. Specifically, the presented protocol is a repeated, sequential model where offers are iteratively exchanged. Under this protocol, agents are assumed to be fully committed to their utterances and utterances are private between the two agents. The protocol is distributed, symmetric, supports bi and/or multi-agent negotiation as well as distributive and integrative negotiation. In addition to coordinating the agent interactions through normative rules, a set of mechanisms are presented that coordinate the deliberation process of the agents during the ongoing negotiation. Whereas the protocol normatively describes the orderings of actions, the mechanisms describe the possible set of agent strategies in using the protocol. These strategies are captured by a negotiation architecture that is composed of responsive and deliberative decision mechanisms. Decision making with the former mechanism is based on a linear combination of simple functions called tactics, which manipulate the utility of deals. The latter mechanisms are subdivided into trade-off and issue manipulation mechanisms. The trade-off mechanism generates offers that manipulate the value, rather than the overall utility, of the offer. The issue manipulation mechanism aims to increase the likelihood of an agreement by adding and removing issues into the negotiation set. When taken together, these mechanisms represent a continuum of possible decision making capabilities: ranging from behaviours that exhibit greater awareness of environmental resources and less to solution quality, to behaviours that attempt to acquire a given solution quality independently of the resource consumption. The protocol and mechanisms are empirically evaluated and have been applied to real world task distribution problems in the domains of business process management and telecommunication management. The main contribution and novelty of this research are: i) a domain independent computational model of negotiation that agents can use to support a wide variety of decision making strategies, ii) an empirical evaluation of the negotiation model for a given agent architecture in a number of different negotiation environments, and iii) the application of the developed model to a number of target domains. An increased strategy set is needed because the developed protocol is less restrictive and less constrained than the traditional ones, thus supporting development of strategic interaction models that belong more to open systems. Furthermore, because of the combination of the large number of environmental possibilities and the size of the set of possible strategies, the model has been empirically investigated to evaluate the success of strategies in different environments. These experiments have facilitated the development of general guidelines that can be used by designers interested in developing strategic negotiating agents. The developed model is grounded from the requirement considerations from both the business process management and telecommunication application domains. It has also been successfully applied to five other real world scenarios

Negotiation Support Systems and Software Agents in E-Business Negotiations

Experiments with negotiation software agents’ in frictionless commerce indicate potential for destructive behaviour. Most of the agents are capable of engaging in auctions and have no ability to conduct complex business negotiations. Recognizing that people and software agents operate in different although overlapping spheres we propose an environment in which negotiation and decision support systems work together with software agents in electronic negotiations. Based on our experiences with the Inspire system we constructed an environment comprising software agents, and negotiation and decision support systems. One agent monitors the process, facilitates the use of Inspire, interprets the negotiators’ activities and provides methodological advice. The architecture of this environment is based on the separation of user support functions from the autonomous software activities, separation of the support for individuals from facilitation and mediation; and scalability and the ability to provide linkages with the existing software

Fairness Issues in Negotiation: Structure, Process, Procedures and Outcome

One of the objectives of IIASA's Project on the Processes of International Negotiation PIN) is to examine how fair and mutually beneficial agreements on transboundary environmental problems can be reached, especially between parties who each wish to satisfy their own interests and their own criteria of fairness and equity which may not coincide. Scientifically reasoned solutions to transboundary problems -- the type that IIASA models often generate -- may represent ideal answers, but are practical only if they satisfy the disputants' principles of justice and fairness. Thus, a critically important dimension of negotiation analysis must be to identify these basic principles and assess the opportunities for convergence in fairness beliefs among disputants. If this can be accomplished, the scientific tools used to explore alternate scenarios and strategies can be more sharply honed to reflect the realities of national interests and acceptability by framing the problem and reasonable solutions in a practical light. This paper reviews the literature and develops a framework for conceptualizing the role played by fairness in international negotiation. It is planned that applications of this framework will be performed concerning transboundary environmental conflicts. A prime candidate for application will be the case of long-range transboundary air pollution in Europe, where PIN Project staff can collaborate with analysts working on IIASA's Regional Acidification Information and Simulation (RAINS) model