2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops Proceedings), Hong Kong, China
A Classification Structure for Automated Negotiations Ricardo B¨uttner Information Systems II, Institute of Business Administration University of Hohenheim, 70593 Stuttgart, Germany
[email protected]
Abstract
literature investigation of negotiation phenomena of organizational approaches in economics.
To date, a lot of automated negotiation models have been developed and manifold negotiation challenges have been already addressed. However, in order to evaluate the state of the art of automated negotiations a classification structure is needed. This paper proposed a taxonomy for classifying automated negotiations. The evaluation of the state of the art to this taxonomy shows the manifold addressing of negotiation challenges, but mainly related to the structure and the process of the negotiation. However, criteria of theoretic foundations and specific negotiation phenomena seem to be inadequately researched.
2. Classifying Automated Negotiations The paper mainly analyzed the works [6, 8, 38, 64, 73, 81, 83, 100, 113, 134, 138]. The main criteria for classifying automated negotiations can be summarized as follows (Fig. 1):
1. Motivation and Problem Description Electronic commerce, particularly automated negotiations, are phenomenal growing fields, e. g. [37, 39, 65, 66, 93]. Systems in these fields have to implement real business requirements that are increasing as well. To date, a lot of automated negotiation models have been developed and manifold negotiation challenges have been addressed. However, in order to evaluate the state of the art of automated negotiations, a classification structure is needed. The research has shown different criteria for classifying automated negotiations [100]. That is why this paper identifies the main criteria to classify automated negotiations and proposes a consolidated criteria structure. This structure is useful for evaluating the research status as well as for giving an overview, especially to new researcher in the field of E-Commerce. The paper is separated into 3 parts: After the problem description, the following part 2 analyzes the main criteria for classifying automated negotiations on the basis of a literature investigation and makes a proposal for a consolidated criteria structure. Part 3 identifies the future need for research based on a reflection of the negotiation models that have been developed to date. The reflection is based on a
Figure 1. Main criteria for classifying Automated Negotiations
2.1. Criteria for the Negotiation Structure One of the most common criteria is the protocol category. It describes the number of negotiating partners [8, 84]. There, bilateral, one-sided multilateral and double-sided multilateral negotiations are separated [83]. Bilateral automated negotiations between self interested
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2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops Proceedings), Hong Kong, China
agents were first analyzed by [87, 88, 144] on the basis of [130]. This type is restricted to two negotiation partners (one buyer and one seller). At first view, it seems to be a very simple negotiation situation. But the trial to integrate real world phenomena (for instance private information or fraud) makes these kind of negotiations very complex [45, 83]. In contrast, one-sided multilateral negotiations (e. g., [41, 122] or ICOMA Auction [61]) are either characterized by one seller and many buyers or by many sellers and one buyer [83, p. 611]. This type of negotiation is deemed to be the standard form of auctions [64, p. 37]. Double-sided multilateral negotiations are characterized by many buyers and many sellers (e. g., [137] based on [88], [125] based on [57] or [142]).
characterized by a direct communication between the participants, e. g., DiCarta [25] or INSPIRE/INSS [43, 44]. In a mediated negotiation, a broker (intermediator) supports the negotiation process or the negotiating partners, for example [10, 34, 48, 58, 80, 120, 125]. In addition, the broker can also control the market process [19].
Concerning the distribution type, automated negotiations can be differentiated in distributed and integrated models [42, 131, 134]. In case of the distributed model, every negotiation partner tries to maximize his own utility (win-lose approach). In contrast, the target of integrated models is to maximize the collective utility of all partners (win-win approach).
Despite of the fact, that the automating of negotiations was forecasted by [22] more than 20 years ago, the question of the possible automation level is still discussed in a very controversial way [6, 45, 79]. The state of the art in electronic negotiations shows full-automated, process support and hybrid negotiation models [83]. Fullautomated models are well structured in order to give the software agents the possibility to negotiate autonomously [8, p. 320]. This type of negotiation is result-oriented. In contrast, process support models facilitate the negotiation (e. g., INSPIRE/INSS [43, 44], CrossFlow [49], CBSS [140] or http://www.ebay.com/). Here, the human participants make the final decision regarding the negotiation result. Concerning the support models, M. Schoop et al. [101] differentiated between document- and communication-oriented approaches. Communication-oriented approaches support the communicative processes involved in any negotiation. Document-oriented approaches offer the possibilities for document exchange and document management. But, e. g., the negotiation support system Negoisst [101] combines both approaches. Finishing, hybrid models are partly-automated, e. g., [1, 23, 123, 139].
Attributes are the characteristics of the negotiation item, that are taken into account during the evaluation. There are two attribute types: single- and multi-attribute. In a single-attribute negotiation the negotiation item is evaluated by one characteristic, normally the price. All other attributes, for instance quality or warranty, have to be agreed in advance. They are not negotiated. Single-attribute negotiations are shown by [29, 41, 120]. In contrast, multi-attribute negotiations are the regular case in non-electronic commercial everyday life [83, p. 612]. This type of negotiation takes simultaneous more than one characteristic into account, e. g., [1, 7, 24, 25, 30, 31, 32, 33, 43, 44, 46, 50, 51, 58, 85, 115, 125, 143]. The number of positions describes the quantity of independent items in a single negotiation over there a final decision is made. The number of positions is independent to the number of attributes or the volume. Automated negotiation systems with the possibility to contract a high number of positions are of significant practical relevance [9, 47]. To solve the complexity problem caused by a high number of positions, normally bidding or auction packages are manually defined in order to get a combined product package, for example Quotes [86] or [63]. In the research area, most of the automated negotiation models do not support a high number of positions [83, p. 613], exceptions are: [2, 5, 11, 23, 115, 121, 122]. Furthermore, negotiations can be separated regarding their mediation type: A non-mediated negotiation is
Regarding the access type, a negotiation can be separated in public and closed sessions [8, p. 318]. In public negotiations new participants can take part dynamically. That is not allowed in closed sessions.
2.2. Describing the Negotiation Process
Regarding the orientation type, [134] presented a differentiation concerning the negotiation process. There, norm-oriented and goal-oriented negotiations are distinguished. Norm-oriented negotiations are characterized by negotiators who act on the basis of obligations and social commitments resulting from negotiation steps. In contrast, the goal-based form is marked by negotiators who act on the basis of expressed goals and objectives. Furthermore, electronic negotiations can be distinguished by the binding type. Binding negotiations (e. g., http://www.ebay.com/) need an authentication of every participant in advance [6, p. 248]. T. W. Sandholm and V. R. Lesser analyzed binding negotiations in [96, 97]
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and presented the so called LCCP (Leveled Commitment Contracting Protocol). LCCP allows a participant to broke a commitment unilaterally. There, the commitments vary continuously from unbreakable to breakable. To every commitment, specific breaking costs are assigned. For example, LCCP is used in the system eMediator [95]. In contrast to binding negotiations, non-binding negotiations do not need an authentication.
negotiation process. One of the strongest advantages of argumentation-based approaches is the ability of an agent to evaluate and possibly correct their own point of view by using the arguments of the negotiation partner [136, p. 148 f.]. Further, the ABN approach increases the possibility and the quality of an agreement compared to the game-theoretic or the heuristic approach [40, 81].
2.4. Criteria for Restrictions 2.3. Theoretic Foundation Criteria [37, 38, 81] differentiate automated negotiations regarding their theoretical approach: game-theoretic, heuristic and argumentation-based approaches are separated. The game-theoretic approach tries to find the optimal strategy between identical agents by the analysis of the equilibrium conditions [56, 70, 71, 72, 99, 102, 103]. On the basis of the fundamental game-theoretic work [130] by J. L. von Neumann and O. Morgenstern, J. S. Rosenschein and G. Zlotkin were the first who analyzed strategic interactions between self-interested agents [87, 88, 144]. Their formal analysis is based on the distributed problem solving approach (see [13]) based on the work of J. C. Harsanyi et al. [35, 36] and D. M. Kreps et al. [55]. Game-theoretic models are deemed to be mathematically elegant, but are very restricted in use because of their assumptions of perfect rationality, unlimited resources and a perfect information situation [21, 37, 39, 74, 78]. Heuristic approaches solve the problematic assumption of unlimited resources via thumb rules, for example [27, 52]. There, the results do normally not correspond to the optimal solutions. Thus, the assumption of perfect rationality is also rejected. Automated negotiation models based on heuristic approaches need an intensive evaluation, regular via simulation or empirical analyses [37, p. 210]. In game-theoretic or heuristic approaches, agents transfer only one information characteristic: offers in terms of potential agreements. In contrast, in argumentationbased negotiations (ABN), the agents have the possibility to reason their positions. When the negotiation partner is persuaded, he will change his negotiation position. The argumentation-based approach was first realized by K. P. Sycara in the negotiation support system PERSUADER [116, 117, 118, 119]. PERSUADER tries to resolve target conflicts between agents by using historical information to persuade the negotiation partner. S. Parsons et al. developed an argumentation-based negotiation model in [76, 77]. The implementation of the model and the design of the different mental attitudes (see belief-desires-intentions model [14]) were shown by [91]. In [106, 107], C. Sierra et al. showed how arguments can be integrated in the general
A. R. Lomuscio et al. [64] differentiate in their classification scheme between complete and incomplete information situations. But, the incompleteness of information is only related to the negotiation partner. For example, the time limit of the negotiation process or the utility function of the negotiation partner can be modeled as private [64, p. 38]. As first, [88] analyzed scenarios with incomplete information. D. Neumann et al. [73] differentiate the information situation related to the negotiation item (open versus closed order books). [17] distinguished incomplete information and/or information circumstances fraught with risk regarding the negotiation item, the negotiation partner and the environment. Time has enormous influence to negotiations [114]: At first, S. Kraus et al. [54] took time limits into account in automated negotiations. Accordingly, e. g., [4, 29, 30, 31, 32, 58, 125, 132, 133] include time limits in their negotiation models. Besides the duration of the negotiation process, timing plays an important role in (automated) negotiations [110].
3. Summary and Future Research Directions Through the increasing importance of electronic markets [64, 105, 145], the requirements of automated negotiation systems increase significantly. The results of this paper show the manifold addressing of negotiation challenges, but mainly related to the structure and the process of the negotiation. However, criteria of theoretic foundations and specific negotiation phenomena seem to be inadequate researched. To address this point, a reflection of negotiation phenomena of organizational approaches seem to be reasonable.
3.1. Direction A: Bounded Rationality The concept of bounded rationality by H. A. Simon [108] is a basic assumption of the most microeconomic organizational approaches (extensions of game theory [90, 92, 112], behavioral-decision-theory [109, 127, 128], systemoriented approach [129], principal-agent-theory [89]). In principle, the main cause of bounded rationality is the
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limitation of resources: E. g., [59, 126, 132] restricted the computational resources, [125] assumed communication costs and [12] information costs above zero. In general, bounded rationality can be modeled via simplifications: Most of the approaches related to bounded rationality, model these by heuristics or rather rules of thumb, e. g., [3, 16, 28, 53, 82, 124, 69, 94, 104, 143]. [98, 142] used heuristic search algorithms, [41, 104] heuristic biddingstrategies. [18] used heuristics based on genetic algorithms. In [90], bounded rationality was modeled by the explicit reduction of the negotiation process. [7] modeled bounded rationality through a predefined utility function. Another way to represent bounded rationality was provided by probability-based models, for example [75, 132]. Finally, the fuzzy approach [141] also presents a possibility for simplifications, e. g., [67, 133]. To date, most of the automated negotiation work have focussed on modeling of bounded rationality by heuristics in order to simplify real world phenomena. But in fact, automated negotiation systems have to handle with strategic behavior of the negotiation partner in spite of bounded rationality or rather limited computational resources. There is a future need of research for new models.
3.2. Direction B: Imperfect Information Although the mathematical-normative branch of the decision-oriented approach [60] and basic works of game theory [130] assume perfect information and thus a decision situation of certainty, the result of the reflection shows that the majority of the organizational approaches (behavioraldecision-theory [20, 68], system-oriented approach [129], transaction-costs-theory [135], principal-agent-theory [89] and extensions of game theory [35, 55]) imply an incomplete and/or an information situation of uncertainty. Despite of the fact, that imperfect information situations play an important role, most of the automated negotiation works have focussed only on the negotiation partner [17]. The negotiation object and the negotiation environment have been inadequate considered, exemplary exceptions: [15, 69, 58, 67, 126]. The handling of incomplete or uncertain information situations related to the negotiation object or the negotiation environment should be a future research direction within the field of automated negotiations.
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