A Comparative Study of Negotiation Decision Support Systems

Download PDF
7 downloads 281 Views 103KB Size Report
Comment
Negotiation Support Systems (NSS) have traditionally supported the process of negotiation rather than modeled the decision-making aspects of the problem. A.
A Comparative Study of Negotiation Decision Support Systems Emilia Bellucci and John Zeleznikow Database Research Laboratory, Applied Computing Research Institute, La Trobe University, Bundoora, Victoria, Australia, 3083. Phone: +61.3.9479 1003; Fax: +61.3.9479 3060; E-mail: {bellucci, johnz}@latcs1.cs.latrobe.edu.au

Abstract Negotiation Support Systems (NSS) have traditionally supported the process of negotiation rather than modeled the decision-making aspects of the problem. A Negotiation Decision Support System is similar to a traditional NSS, but introduces an element of decision support by proposing offers or compromises. The decision support aspect can be improved through the use of machine intelligence, to sufficiently model the complicated and dynamic nature of the domain. Our research has focused upon the domain of Australian Family Law. We discuss, compare and contrast four systems we have built in our laboratory, Family_Negotiator, Split_Up, AdjustWinner and DEUS. In doing so, we learn the complexities of the negotiation domain, and investigate modeling issues for construction of an intelligent negotiation support tool. Our research has included the study of negotiation strategies and how to build decision support systems to help support humans use such strategies. Our future research will involve multi-criteria decision making, genetic algorithms and graph theory to build Negotiation Decision Support Systems (NDSS).

1. Introduction Negotiation is conceptualized as a means through which two or more purposive actors arrive at specific settlements or outcomes under conditions of strategic interaction or interdependent decision making [1]. Negotiations occur in a variety of political, economic and social settings [2].

The negotiation process might be formal or mandated as in legal and industrial disputes, semi-formal, as in international disputes, or totally informal as in the case of two prospective partners negotiating as to how they will conduct their married life [3]. [4] stress that although negotiation is a human problem, computers are already at the bargaining table to transform the negotiation process. The term Negotiation Support Systems (NSS) has traditionally referred to computers that guide the negotiators towards a suitable settlement. This has forced the emergence of utility functions and other statistical methods to represent the state of negotiations at some point in time, but not so of decision making concepts and functions. In this paper we will briefly describe several different systems which model some aspect of negotiation built in our laboratory and operating in the domain of Australian Family Law. DEUS is a template-style negotiating tool, which represents the status of negotiations at some point in time. Split_Up utilizes in-built rules and neural networks to model discretionary decision making. AdjustWinner is similar to Split_Up in that it is considered a decision support tool to be used during negotiation, and not model the process itself. Family_Negotiator models the negotiation process by allowing users to enter their issues and the interests behind them; and hence utilizing Principled Negotiation theory [5] to do so. It then retrieves the best resolution to the problem through a text-based matching system. Our analysis of existing negotiation support systems has revealed several serious limitations of current systems. This includes their inability to learn from experience, draw reasonable conclusions from the given information, and provide helpful explanations to the user.

1060-3425/98 $10.00 (C) 1998 IEEE

2. Traditional versus decision supported Negotiation Support Systems A NSS is a computer system that supports human negotiation. Traditional NSSs have been template-based with little attempt made to provide decision making support. When a traditional NSS models the negotiation as a process only, it acts as a template to display details of previous offers by either or both parties. The way the data is represented to the user is important so users can construct better offers. INSPIRE [6] used utility functions to graph offers; while in DEUS [7] the goals of parties (and their offers) were set on screen side by side. The primary role of these systems is to provide users with a guide to how close (or far) a negotiated settlement is. [7] developed a model of family law property negotiation, which relies upon building a goal for each of the litigants, with the goals being supported by their beliefs. Goals can only take real number values, because in simplifying the model it is assumed that the goal of each party is a monetary figure. Beliefs, which support the goals, are expressed in natural language. In the system, which has been implemented using this model, goals are used to indicate the differences between the parties at a given time. The beliefs provided are used to support the goals. The model calculates the agreement and disagreement between the litigants’ beliefs at any given time. The agreement and disagreement are only in relation to the beliefs and hence do not resolve the negotiation. In order to reach a negotiated settlement, it is essential to reduce the difference between the goals to nil. Having defined the model, it was implemented into DEUS. The system supports the negotiation process by representing the goals and beliefs of the opposing parties to a property conflict arising from a divorce application. It helps mediators understand what issues are in dispute and the extent of the dispute over these issues. INSPIRE [8] is a research tool operational on the World Wide Web (WWW). It supports negotiations by modeling the three main stages of a negotiation; that of preparation, offer-exchange and post-settlement [8]. While INSPIRE had been implemented to collect data on cross-cultural negotiations and study the impact of decision analysis on negotiations, it has also been quite successful as a facilitator of negotiation across the Internet. Negotiators communicate by exchanging offers and electronic mail, and through a utility graph function,

they can check the closeness of a package to their initial preferences. This storing function helps in the construction of offers. It contains a facility for specification and assessment of preferences, and graphically displays progress made. A decision support negotiation system is a system which supports negotiation in a similar manner to a traditional NSS, but by interpreting the goals, wants and needs of the parties and by analyzing past offers and counteroffers by both parties, the system can propose sample settlements. Recently, research systems have been developed which use artificial intelligence techniques to provide decision support for human negotiators. These have tended to be domain specific, such as in family law (Split—Up [9] and Family_Negotiator [10]) industrial relations (PERSUADER [11], NEGOPLAN [12] and ARBAS [13]) and international disputes (MEDIATOR [14] and GENIE [15]). Originally NEGOPLAN [16] was a rule-based system written in PROLOG which advised upon industrial disputes in the Canadian paper industry. Subsequent work using distributed artificial intelligence techniques have enabled NEGOPLAN to provide more sophisticated generic negotiation advice. GENIE integrates rule-based reasoning and multi-attribute analysis. MEDIATOR used case retrieval and adaptation to propose solutions to international disputes whilst PERSUADER integrated case-based reasoning and game theory to provide decision support with regard to United States' industrial disputes.

2.1.

Family_Negotiator

The Family_Negotiator system is a hybrid casebased/rule-based system operating in three distinct areas of negotiation in Australian Family Law: 1. Determining child custody — for which a case-based reasoner is used due to the open textured nature (knowledge which is incomplete or uncertain in the legal arena) of the domain; 2. Determining contents of common pool; that is what property the court is empowered to distribute — for which a rule-based reasoner is used; 3. Determining the percentage of the common pool each party receives, that is the percentage split of the common pool — for which a hybrid rule-based/case-based reasoner is used. In Family_Negotiator experiences are stored as cases to be referenced by the case—based reasoner. Rules are

1060-3425/98 $10.00 (C) 1998 IEEE

stored and referenced within the relevant subsections of the program that support rule—based reasoning. Family_Negotiator requires the user to enter as input those issues which are yet to be resolved. Each issue is dealt with separately by different modules. Information passing from the user interface to negotiation modules is the number calling each negotiation procedure. After each issue has either been successfully negotiated or an impasse arises, the next issue commences. The cycle ends when Family_Negotiator has dealt with all issues. When either party terminates an issue, a failure is recorded. Any failure as well as successful negotiation can be used in determining outcomes of later cases presented to Family_Negotiator.

2.2.

Split_Up

Split—Up is a hybrid rule-based neural network system, which provides advice on property distribution upon divorce in Australia. Rules are used to determine the contents of the common pool. A combination of rules and neural networks are used to determine the percentage split between parties. For example, the level of wealth of a marriage is determined by a rule, which uses the common pool value. The percentage split determination uses a neural network that learns from the relative contributions of the litigants, the relative needs of the litigants and the level of wealth of the marriage. The Split—Up system involves a hierarchy of ninetynine relevant factors for percentage split determination [17]. The hierarchy provides a structure that was used to decompose the task of predicting an outcome into thirtyfive sub-tasks. Outputs of tasks further down the hierarchy are used as inputs into sub-tasks higher up in the hierarchy. The inferencing of twenty-one sub-tasks were performed with a neural network, whilst for the remaining fourteen sub-tasks, small rule sets were used. The principal obstacle to the use of neural networks in the legal domain is that the explanations for inferences cannot be generated. This problem was overcome by embedding the neural network within a knowledge representation framework based on the structure of arguments proposed by [18]. In [19] we discussed how DEUS, Family_Negotiator and Split_Up interpret the following hypothetical example. In this paper we will run the same example through another system we have constructed AdjustWinner. We will then compare how all four systems operate.

3. A hypothetical example from Family Law Let us suppose Amanda(W) and Paul(H) Jones have been married for fifteen years and have two sons aged thirteen and eleven. Amanda wants a divorce and an immediate property settlement. She also believes that although she received income from employment throughout her marriage, her principal role was as a homemaker and a nurturer. She believes the joint marital property consists of a house (worth $400,000), her Volvo car (worth $20,000), and his Porsche car (worth $100,000). In addition, she believes she is entitled to a portion of her husband’s share in his medical practice (which she values at $1,000,000) and of his superannuation entitlements (which she values at $200,000). She wishes to retain the house and the Volvo. She believes she should receive custody of the children. She consults a lawyer who advises her that as custodian she should seek 60% of joint property and adequate child allowance. They agree that the two cars, house, medical practice and superannuation investments should all be included in the common pool. They value joint property at $1,720,000. Thus Amanda’s share is valued at $1,032,000. She hence asks for the house, Volvo and $612,000. Her requirements and Paul's counter demands are described in the following tables. Agreement • Marriage lasted fifteen years. • There are two sons, aged thirteen and eleven. • P earns $25,000 per annum in her job as a legal secretary. • They jointly own a Volvo worth $20,000.

1060-3425/98 $10.00 (C) 1998 IEEE

Disagreement W’s beliefs Whilst throughout the marriage W had a job, this was secondary to caring for her children and supporting H in his medical practice H earns $200,000 per annum from his medical practice They jointly own a Porsche worth $100,000 They jointly own a house worth $400,000 H has superannuation investments of $200,000 H’s share in his medical partnership is worth $1,000,000 W was the primary care giver to the sons W requires 60% of the common pool

H’s beliefs Throughout the marriage W had a job, which affected her support for H and the children. W provided no support for H in running his busy medical practice. H earns $80,000 per annum from his medical practice The Porsche is owned by H’s medical practice They jointly own a house worth $600,000 H has superannuation investments of $100,000 H’s share in his medical partnership is worth $500,000 W and H shared parental duties H requires 50% of the common pool

Figure 1. Agreement and Disagreement between husband and wife in hypothetical example.

3.1.

AdjustWinner

The paradigm used in AdjustWinner was developed by [20]. We shall focus upon disputes between two parties: however, the principles can easily be extended to disputes between three or more parties. In this paradigm, it is assumed there are k discrete issues in dispute, each of which is assumed divisible. The Adjusted Winner procedure is a point allocation procedure that distributes items or issues to people on the premise of whoever values the item or issue more. The two players are required to explicitly indicate how much they value each of the different issues by distributing 100 points across the range of issues in dispute. [20] claim the Adjusted Winner paradigm is a fair and equitable procedure because at the end of allocation, each party will have accrued the same number of points.

If, as is generally the case, the disputants do not have directly opposing goals, it is likely that each disputant will receive more than 50 points. This is thus an improvement on any strategy that is based on the zero sum game philosophy — where each party wins what the other loses. Often giving an issue or item to one party will lead to an inequality of points amongst the disputants. For the final issue in dispute to be resolved, this generally results in each party receiving a fraction of the item to be allocated. This may mean the item will need to be sold (if possible) and the dividends divided according to the formula determined by the algorithm. If this is not possible, some other form of compensation will need to be determined. AdjustWinner's architecture is governed by a simple formula to calculate the division of issues or items. [20] define the procedure as the allocation of k goods, where X is the sum of all points party A values more than party B does. Let Y be the sum of points party B values more than party A. Assume X >= Y. Next we allocate items so that Party A initially obtains all goods where xi >=yI, and party B gets the others In the next step we need to achieve equitability – that is until the point totals of the two players are equal. The equitability adjustment formula aims to equalize the number of points both players have been allocated. Once the program has finished the calculation, it alerts the users to the items/issues the parties have been allocated and if appropriate will indicate the percentage to be given to both parties of an item that requires further division. Whilst the system suggests an allocation of items, it is up to human negotiators to finalize an agreement acceptable to the disputants. For example if a couple are disputing the custody of children it is impossible to give 75% to the wife and 25% to the husband. However it could be suggested the wife have custody with generous access to the husband. If the system recommended the wife have 75% of the house, this could be achieved by selling the house and giving the wife 75% of the profit. When run through the hypothetical example, AdjustWinner gave 100 points to both H and W to allocated to the seven issues in dispute: custody of children, value of H's annual income from the medical practice, value of the Porsche, value of the house, value of H's superannuation, value of H's share of the medical practice, and percentage of the common pool each partner receives. H and W distribute their 100 points as follows:

1060-3425/98 $10.00 (C) 1998 IEEE

Issue

Points given to issue by H Custody 19 H’s annual income 5 Value of Porsche 9 Value of house 5 H’s superannuation 9 H’s medical practice 24 Percentage of 29 common pool

Points given to issue by W 70 1 1 1 2 20 5

Figure 2. Points given to AdjustWinner algorithm at the first stage of the hypothetical negotiation.

AdjustWinner recommended the following solution: Custody should be given to the Wife, whilst the value of the Husband’s Salary, the Porsche, the Percentage of the Common Pool, the Value of the House and the value of the Husband’s Superannuation should be accepted as requested by the Husband. The indivisible item was the value of the Medical Partnership. The system decided to give the wife 25% of her request and the remaining 75% of his request to the Husband. Thus the Medical Partnership should be valued at 80,000 + 0.25 * (200,000 - 80,000) = 110,000. The total number of points both H and W were allocated was 75. Thus, this solution gives each party 75 % of their goals. If they could agree that they jointly own a house worth $500,000 and that the husband’s superannuation entitlement is currently valued at $120,000 then there are only five issues in dispute: custody of children, value of H’s annual income from the medical practice, value of the Porsche, value of H’s share of the medical practice, and percentage of the common pool each partner receives. H and W distribute their 100 points as follows: Issue

Points given to issue by H Custody 20 H’s annual income 10 Value of Porsche 10 H’s medical practice 25 Percentage of 30 common pool

Points given to issue by W 70 2 3 20 5

Figure 3. Points given to AdjustWinner at second stage of the hypothetical negotiation.

AdjustWinner recommended the following solution: Custody should be given to the Wife, whilst the value of the Husband’s Salary, the Porsche and the Percentage of the Common Pool, should be accepted as requested by the Husband. The indivisible item was the value of the Medical Partnership. The system decided to give the wife 23% of her request and the remaining 77% of his request to the Husband. Thus the Medical Partnership should be valued at 80,000 + 0.23 * (200,000 - 80,000) = 107,600. The total number of points both H and W were allocated was 74.4. If they can further agree that the wife is entitled to $350,000 for giving up her claims on the husband’s medical practice and upon the value of H's annual income, then they are only three issues in dispute: custody of children, value of the Porsche and the percentage of the common pool each partner receives. H and W distribute their 100 points as follows: Issue

Points given to issue by H Custody 30 Value of Porsche 20 Percentage of 50 common pool

Points given to issue by W 80 5 15

Figure 4. Points given to AdjustWinner at the third stage of the hypothetical negotiation.

In this negotiation, the custody issue was valued greatly by the Wife; this meant that if wife were granted custody, she would receive 80 points and the Husband would receive only 20 + 50 points (the points he gave to the Porsche and his required percentage of the common pool). Using the AdjustWinner algorithm, the custody issue would then be the only item to share. At his point, the final allocation formula would be: 70 + 30x = 0 + 80(1 - x). From this we deduce 1/11 = x. Hence, the Husband would receive 70 + 30(1/11) points = 72.73 and the Wife will receive 80 * (10/11) = 72.73 points. Thus whilst the Adjusted Winner procedure reached an equitable solution it is not one that can be easily implemented.

1060-3425/98 $10.00 (C) 1998 IEEE

4. A Comparison of systems The four systems developed in our laboratory (DEUS, Split_Up, Family_Negotiator and AdjustWinner) all operate in the domain of Australian Family Law, and all model some aspect of negotiation. Due to the narrowness of the domain, our study cannot be termed as scientific, but does give us an indication of what some of the modeling should be when implementing future negotiation decision support systems. DEUS is a template-based system which does not make suggestions as the other systems do, but presents the facts as the users have entered them. It then calculates the amount of money the disputants are apart in their requirements. The hypothetical was run three times through DEUS; each time representing a different stage of the negotiation. Split_Up and Family_Negotiator can be considered as intelligent systems since they can generate solutions using the system’s internal knowledge as well as users input. Split_Up’s neural network has already been trained when a case is presented, and uses this knowledge together with rules implemented in a rule-base, to make judgement. In the hypothetical example, Split_Up gives 60% of the distributable assets to the wife if she was granted custody of the children. When the same case was run on Family_Negotiator; it gave custody to the wife and providing this is accepted, suggested the wife receive 60% of the common pool. It is interesting to note that in both trials, the results were the same, but the method of retrieving them was not. Family_Negotiator consulted a case-base to compare the current case with its cases in the case-base, whereas Split_Up used its in-built neural network and rule-base. Retrieval in Family_Negotiator was performed on a text-based matching system, and so to a certain point, the results can be viewed to be very limiting and shallow. Family_Negotiator also failed to take into consideration the effects of issues dependent on others for resolution. In trying to classify these as negotiation systems, Family_Negotiator modeled the different stages of negotiation best by asking for parties’ positions and reasons behind these. Although Split_Up is useful in its attempt to develop one's BATNA, it cannot be considered as a NSS because it does not guide users through the negotiation stages of preparation, assessment of issues and so forth. AdjustWinner is an example of system that makes decisions using the input of the parties only. Unlike

Family_Negotiator or Split_Up, it does not use outside knowledge, and hence cannot be considered as an intelligent decision making tool. The parties using the tool need to be sure of exactly what they want; otherwise there is a risk the system could work to the user’s detriment. AdjustWinner can still be considered as a decision making tool since essentially it gives users a solution to the problem. Like DEUS, AdjustWinner was run three times to model the three stages of negotiation; suggesting the systems were not designed to model the negotiation process. These results were not so easy to implement as they were in the previous systems, mainly because negotiators were asked to input the values they placed on the issues. This meant custody (valued most highly by the Wife) was, at the conclusion of negotiation, the only issue to ‘allocate’. If custody were the only issue to distribute at the start of negotiations, then the negotiation would be difficult to resolve since it is not divisible. AdjustWinner is similar to Split_Up in that it is considered as a tool to be used during negotiation, rather than modeling the process itself. Unlike Split_Up, it works on the principle of isolated issue distribution; which does not leave much room for compromises involving issues whose resolution depends on the distribution of other items or issues. Isolated issue solving is not generally appropriate for the domain of negotiation. By comparing our NSSs, we can begin to develop theories about negotiation support. When building a negotiation system, it is important to build an easily understandable user-interface with good help functions. From this project, we have learnt the difficulties in representing a negotiation, the nature of negotiation data and how such data can be stored and retrieved to propose new solutions. Case-based retrieval used on its own only retrieves shallow and uninteresting solutions. Rule-based reasoning is not flexible enough to model the complexities and dynamics of negotiation, since a negotiation often does not have a particular set of heuristics or structure to follow. Neural networks can almost become too predictable, since we are teaching the neural network to process problems in the particular way it was trained. We need a framework to support new and innovative options from the positions, interests and goal of the parties. Whilst qualitative methods of reasoning should be encouraged, but especially in unstructured domains such as negotiation, we may need to understand the concepts

1060-3425/98 $10.00 (C) 1998 IEEE

of conflict and multiple criteria to develop new resolutions for negotiations. At the next stage of research, we propose to investigate the modeling issues for construction of a negotiation decision support system.

5. Modeling Issues for construction of Negotiation Decision Support Systems When building Negotiation Decision Support Tools, the modeler should take into account two major issues; • The existence of an underlying negotiation strategy. A negotiation strategy is important in helping to identify the stages in a negotiation and to guide negotiators towards agreement. • The importance of discovering the relationship (if any) between the issues under investigation. The negotiation strategy we have employed in Family_Negotiator and have consulted in Split_Up will be used again in the construction of further negotiation support systems. [5] in their discussion of Principled Negotiation consider the different stages of negotiation and in particular that negotiation is modular in design. Their theory makes for easier computer implementation of the strategy than is possible with other negotiation strategies. Whilst there is numerous psychological research as to how humans negotiate, very few concrete results have emerged. The Harvard Negotiation Project of [5] promotes deciding issues on their merits rather than through a haggling process focused on what each side says it will and will not do. The five basic points of the approach are: • Separate the people from the problem — the negotiators should see themselves as attacking the problem posed, not each other. • Focus on interests not positions — your positions are what you want whereas your interests are why you want your positions. By focusing on interests, you may uncover the existence of mutual or complementary interests that will make agreement possible. • Invent options for mutual gain — even if the parties' interests differ, there may be bargaining outcomes that will advance the interests of both. • Insist on objective criteria — some negotiations are not susceptible to a win—win situation. The most obvious of these is haggling over the price of an item:

since the more one side obtains the less their opponent receives. • Know your best alternative to a negotiated agreement (BATNA) — the reason you negotiate with someone is to produce better results than would otherwise occur. If you are unaware of what results you could obtain if the negotiations are unsuccessful, you run the risk of • entering into an agreement that you would be better off rejecting; OR • rejecting an agreement you would be better off entering into. [21] investigate combining Principled Negotiation with Evolutionary Systems Design and Group Support Systems to build Negotiation Support Systems. Split— Up [17] uses Principled Negotiation to determine each side's BATNA in a family law dispute. The BATNA then becomes a starting point for negotiations. Family_Negotiator incorporated Principled Negotiation by making pivotal use of reasons users provided for a position taken.

6. The role of interdependence of issues in building NSSs We need to determine what types of issues make up a negotiation. In particular we need to consider whether the issues are independent, semi-dependent or dependent on each other. It is imperative to focus on the relationships between issues to establish the best method of obtaining a satisfactory settlement. An example of an independent negotiation in family law is where W (wife) wants 50% of a car owned by the couple, regardless of whether H (husband) has custody of the children or not. The issues of splitting the car and child custody can be taken separately and divided without any thought to the effect it may have on resolution of another issue. An example of a semi-dependent negotiation is where the percentage distribution of the house depends on the custody of children. This example illustrates these issues are dependent only until a solution to the custody issue is found. Once this is determined, it will be generally known who will get the larger percentage of the house. An example of a dependent negotiation is when a director is negotiating a dispatching contract to deliver car parts to his panel beating business. Issues in dispute are the price of goods, time of delivery and payment timing. All these issues are negotiable when taken

1060-3425/98 $10.00 (C) 1998 IEEE

collectively; and hence the relationship between these issues is very dependent. A possible settlement will most probably need to accommodate allowances and compromises. That is, a greater price may be asked if there is a 60-day interest-free payment option. Once a relationship between issues in dispute has been identified; we can then proceed to find decision support tools to model the processes. Independent negotiations can be modeled by a similar procedure to that proposed in [20] - the Adjusted Winner procedure. It works well in this case because it allocates issues or items according to the value placed on them by the negotiators. This procedure should be applied when negotiators have given values to the issues. We have implemented the procedure into a system, AdjustWinner, to test its effectiveness in negotiation support. Strategies to support semi-dependent and dependent issues range from the use of Artificial Intelligence techniques, such as neural networks, case-based reasoning, fuzzy theory and other knowledge-based approaches to mathematical approaches encompassing game theory and other axiomatic approaches. There are cases for and against the use of these techniques and we currently are in the process of investigating their applicability. Some ideas yet to be implemented include Multi-Criteria Decision Making and Genetic Algorithms. Multi-Criteria Decision Making (MCDM) involves the use of a single formula (or a series of formulas) to find the optimal solution to a problem involving many criteria. At this present time, it is hard to imagine all negotiations can be modeled by a set formula. Game theory, at the roots of MCDM seems inappropriate for negotiation support since it requires possible outcomes to be known and well specified, eventhough multi-criteria decision making and multi-attribute utility theory have been viewed as ’natural extensions’ of negotiation decision making support [22]. Genetic algorithms work similarly by combining discrete options into many packages. It takes criteria (as given by users) and forms new solutions by introducing statistical theory, in particular combinatorial probability theory. Genetic Algorithms, as with game theory, limit the search space and hence may be useful when building a NSS. Before we decide which decision support tools to use, we need to establish how best to represent the given negotiation. Once we have determined the effect one issue has on another; we can identify the type of solution that would most likely be accepted. For instance, if there

is a certain dependency between issues, it is likely that a compromise would be acceptable as a resolution. Other methods of extracting solutions include bridging, expanding the pie, cost-cutting, compensation and logrolling [23]. A representation is required for internal use of the decision support module, as well as for display purposes (to provide explanations) to negotiators. As a general guide; the inputs and outputs to a decision making module based on the issues raised in this paper are: the effect of issues on each other; the value (importance) placed on the issue by the negotiators and the trend to date of the negotiation. Background on what the negotiator has rejected or partially accepted would give an indication as to how on-going negotiations should proceed. These ideas of re-use of past cases in similar to that which is instrumental to Case-Based Reasoning. Outputs of a decision support module include the most promising package (or several options) and explanations to suggest how the system can make these conclusions. Now that we have outlined the modeling issues particular to decision support of the NSS, we can further proceed implementation of algorithms and functions to test the above theories.

7. Conclusion Negotiation Support Systems (NSS) have been implemented as template-based systems to support the process of human communication in the context of negotiation. The systems we are interested in developing support negotiation in a similar way to how these traditional negotiation support tools do, but also incorporate a decision-making aspect. This decisionmaking aspect comprises of the system contriving suggestions that are most likely to be accepted by both parties. The main objective of our research on building negotiation support tools is to advance the current state of intelligent negotiation support systems and by doing so, lead to the development of systems that can be used in practice. We hence add reasoning capabilities to negotiation support systems using artificial intelligence techniques, such as case-based reasoning and machine learning, argumentation and agent-based systems. Mathematical methods, such as probability theory and fuzzy set theory may be interesting to implement; since we have found qualitative methods (such as case-based reasoning) do not give the domain enough depth to retrieve optimal solutions.

1060-3425/98 $10.00 (C) 1998 IEEE

[12]

8. References [1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

Young, O.R., “Strategic Interaction and Bargaining,” in Bargaining: Formal Theories of Negotiation. Urbana Illinois: University of Illinois Press, 1975. Lim, L. F., Benbassat, I., “From Negotiation to Negotiation Support Systems: A Theoretical Perspective,” presented at Twenty-fifth Annual Hawaii International Conference on Systems Science - Mini track on Negotiation Support Systems, Hawaii, 1992. Bellucci, E., Zeleznikow, J., Antoniou, G., “Intelligent Computer Tools for Supporting Human Negotiation: Systems and Research,” presented at PACIS97 - Proceedings of First Asia-Pacific Conference on Information Systems, Brisbane, Australia, 1997. Chung, W. W. C., Pak, J. J. F., “Implementing Negotiation Support Systems: Theory and Practice.,” presented at Thirtieth Annual Hawaii International Conference on Systems Science - Mini track on Negotiation Support Systems, Hawaii, 1997. Fisher, R., Ury, W., Getting to YES: Negotiating Agreement Without Giving In, Boston: Haughton Mifflin, 1981. G. E. Kersten, “Support for Group Decisions and Negotiations: An Overview,” in Multiple Criteria Decision Making and Support, J. Climaco, Ed. Heidelberg: Springer Verlag, 1997. Zeleznikow, J., Meersman, R., Hunter, D. and van Helvoort, E, “Computer tools for aiding legal negotiation,” presented at ACIS95 - Sixth Australasian Conference on Information Systems, Curtin University of Technology, Perth, Western Australia, 1995. G. E. Kersten, S. R., Noronha, “InterNeg: A Web Site for and about Negotiation” http://interneg.carleton.ca/interneg/research/in_97.ht ml, 1997. Stranieri, A., Zeleznikow, J., “The Split_Up system: Integrating neural networks and rule-based reasoning in the legal domain” presented at Fifth International Conference on Artificial Intelligence and Law, 1995. Bellucci E., Zeleznikow, J., “Family_Negotiator: an intelligent tool for supporting human negotiation,” presented at Research and Development in Expert Systems XIII. Expert Systems 96, Cambridge, 1996. Sycara, K., “Negotiation planning, An AI approach,” European Journal of Operations Research, vol. 46, pp. 216-234, 599, 1990.

[13]

[14]

[15]

[16]

[17]

[18] [19]

[20]

[21]

[22]

[23]

Kersten, G. E., Michalowski, S., Szpakowicz, S. and Koperczak, Z, “Restructurable Representations of Negotiation,” Management Science, vol. 37, pp. 1269-1290, 1991. Bodart F., Bui, T., Melard, P and Vanreusel, J. F., “ARBAS'96: A System of Argumentation Support and Organizational Memory,” presented at Thirtieth Annual Hawaii International Conference on Systems Science - Mini track on Negotiation Support Systems, Hawaii, 1997. Kolodner, J. L., Simpson, R. L., “The Mediator: Analysis of an Early Case-Based Problem Solver,” Cognitive Science, vol. 13, pp. 507-549, 1989. Wilkenfeld, J., Kraus, S., Holley, K. M. and Harris, M. A., “GENIE: A decision support system for crisis negotiations,” Decision Support Systems, vol. 14, pp. 369-391, 1995. Matwin S., Szpakowicz, S., Koperczak, Z., Kersten, G., E., Michalowski, W., “Negoplan: An Expert System Shell for Negotiation Support,” IEEE Expert, pp. 50-61, 1989. Stranieri, A., Zeleznikow, J., Gawler, M. and Lewis, B., “A hybrid-neural approach to the automation of legal reasoning in the discretionary domain of family law in Australia,” Artificial Intelligence and Law, 1997. Toulmin S., The uses of argument. Cambridge: Cambridge University Press, 1958. Bellucci E., Zeleznikow, J, “Family_Negotiator: an intelligent decision support system for negotiation in Australian Family Law,” presented at Fourth Conference of the International Society of Decision Support Systems, Lausanne, Switzerland, 1997. Brams, S., J., Taylor, A., D., Fair Division, From cake cutting to dispute resolution. Cambridge, UK. Cambridge University Press, 1996. Lewis, L., H., Spich, R., S., “Principled Negotiation, Evolutionary Systems Design and Group Support Systems: A Suggested Integration of Three Approaches to Improving Negotiations,” presented at Twenty-ninth Annual Hawaii International Conference on Systems Science - Mini track on Negotiation Support Systems, Hawaii, 1996. Zionts, S., “The State of Multiple Criteria Decision Making: past, present and future.,” presented at Multi-Criteria Decision Making: Ninth International Conference, 1992. Wertheim, E., Love, A., Littlefield, L., Peck, C., I Win: You Win, How to have fewer conflicts, better solutions and more satisfying relationships. Ringwood, Victoria: Penguin Books, 1992, pp. 130135.

1060-3425/98 $10.00 (C) 1998 IEEE