Imène Brigui-Chtioui 1, Inès Saad 2

Transcription

1 A MULTIAGENT APPROACH FOR GROUP DECISION MAKING IN KNOWLEDGE MANAGEMENT Imène Brigui-Chtioui 1, Inès Saad 2 1 GRIISG Institut Supérieur de Gestion imene.brigui-chtioui@isg.fr 2 MIS, UPJV, Amiens School of Management Abstract. In this paper we propose an argumentative multiagent model based on a mediator agent in order to automate the resolution of conflicts between decision makers for identifying knowledge that need to be capitalized and that we call "crucial knowledge". We follow both an argumentative approach and a multi-agent system based on a mediator agent. This new approach allows the mediator agent to elicit preference of decision makers which can be different or even contradictory while exploiting and managing their multiple points of view to identify crucial knowledge. Concrete experiments have been conducted on real data from an automotive company and on randomly generated data. We have observed that a non-argumentative approach is more sensitive to the variation of the number of knowledge than an argumentative one. Indeed, the classification results using the multiagent system are consistent with classifications of human decision makers in nearly 80% of studied cases Keywords: Knowledge Classification, Multi-agent Systems, Argumentation, Conflict resolution, Knowledge Management, Decision Support System.. 1. Introduction The necessity to create and to use knowledge mobilized and produced in firms has increased rapidly these last years. Firms become aware of the importance of the immaterial capital owned by their employees which corresponds to their experience and accumulated knowledge about the firm activities. Maintaining this capital is powerful mean to improve the level of performance of the firm. In order to create, preserve and share knowledge in firms, Knowledge Management has been occupying since the beginning of the nineties a more and more important place within organizations. Thus, companies should invest in engineering methods and tools (Dieng-Kuntz et al., 1999) in order to preserve knowledge especially those of tacit nature. Researchers in knowledge engineering and knowledge management have been focusing on the problems of acquisition, preservation and transfer of knowledge. However, considering the large amount of knowledge to be preserved, the firm must first determine knowledge that should make the object of capitalization. We should focalize on only the so called crucial knowledge, i.e. the risk of their lost and the cost of their (re)creation is considered important; their contribution to reach the project objectives is very important and their use duration is long. Not enough works exist concerning the identification of knowledge on which it is necessary to capitalize (Grundstein, 2000; Tseng and Huang, 2005), thus, we have proposed a multicriteria method based on dominance rough set approach to identify and qualify crucial knowledge in order to justify a situation where knowledge capitalization is advisable. The value added of our methodology is to elicit the preference of the decision makers. This method is supported by a decision support system called K-DSS (Saad et al., 2005). Our system K- DSS is based on two types of tasks: automation task and human task. Moreover, because of the large amount of knowledge to analyze, the large number of decision makers involved in the assignments of knowledge, contradictory opinions that decision makers can have (that lead to inconsistencies in the shared knowledge base) and also usually hard delay constraints of projects, it is necessary to automate the resolution of conflicts between decision makers. The aim of this paper is to use multiagent theory and an argumentative approach to cope with inconsistency in decision rules in our decision support system. In this work, we present our multiagent system (Brigui-Chtioui and Saad, 2009) which is made up of a set of autonomous behaviorbased agents that act on behalf of their beliefs.

2 The rest of the paper is structured as follows. Section 2 provides an overview on the related works. The multiagent system details in section 3. The experimentations and results are presented in section 4. Section 5 summarizes our contribution and outlines some of our future works. 2 Research studies In the literature, there are several authors providing information systems based on multiagent architectures such as Spanoudakis and Moraitis (Spanoudakis et al., 2007). The authors propose an architecture which is based on a multiple FIPA agents interacting in order to provide infomobility services to mobility impaired people. This study is novel in the sense that it takes into account the needs of different types of users (or combinations of these types), and that involves reasoning which uses different and possibly conflicting knowledge describing the needs of these types. Many researchers are interested in proposing solutions to problems of classification based on multi-agent approaches. Amgoud and Serrurier (Amgoud and Serrurier, 2008) have proposed the first framework for classification that is completely argumentation-based by using and adapting Dung s semantics (Dung, 1995). An argumentative approach has been also used for classifying concepts and examples by Gomez and Chesnevar (Gomez and Chesnevar, 2004). In their work, the authors notice that existing classification models based on neural networks may classify the same example in different classes. In such case, a random choice is made for choosing the class to keep. The authors have then proposed a hybrid approach that applies first the neural networkbased model. In case of conflicts, when the same example is classified in different classes, an argumentation system is used to make the final choice in a rational way. In Chesnevar et al. (Chesnevar et al., 2008), The authors propose a multiagent approach to solve the problem of knowledge distribution in large organizations, based on integrating in a multiagent Knowledge Management system a logic programming formalism for defeasible argumentation. By representing power and trust capabilities associated with the agents involved, they solve conflicts emerging from potentially contradictory policies as well as from trust and empowerment issues. Several studies propose architectures based on a mediator agent such as automated negotiation (Bichler, 2000). Indeed, this architecture has the advantage of minimizing the messages exchanged in order to find a mutual agreement. Our context of study lends itself easily to this solution. In our context, the number of decision makers and the amount of knowledge on which they must agree can be important, which can induce high delays and important algorithmic costs. 3 The Multiagent system As we said before in the real organization where the project is complex it is very difficult to use a constructive approach like the approach proposed by Belton and Pictet to solve conflict between decision makers and determine a collective decision rules. Indeed, it is very difficult to contact all decision makers who are sometimes dispersed geographically to assign a large number of knowledge to be evaluated, especially when there are hard delay constraints. Our multiagent system (Figure 1) is made up of a set of autonomous behavior-based agents that act on behalf of their beliefs. The organization contains two types of agents: a. The mediator agent m that is responsible for the knowledge base management. Its goal is to solve conflicts in order to have a consistent knowledge base. It detects conflicts and connects agents that are source of these conflicts, then prompt them to reach an agreement. If an agreement is not reached, the mediator agent makes an objective decision using its meta-rules. Note that only the mediator agent is allowed to modify the collective knowledge base. The meta-rule notion will be detailed in section 4.4. b. The decision maker agents a i that are responsible for the knowledge classification on the basis of its beliefs. Each decision maker agent represents a human decision maker and manages an individual rule base allowing it to perform classification and argumentation. Agents involved in the knowledge classification process have the same goal: Sharing a consistent knowledge base. Decision maker agents are made up of 3 interdependent modules: - Communication module allowing message exchanges between agents; - Inference module responsible for inferring rules from the individual rule base and deducing classification for each knowledge; - Argumentation module which is able to construct arguments that enhance a given classification.

3 The communication module is in relation with the argumentation module in order to construct messages to be sent to the other decision maker agents. The argumentation module is in relation with the inference module which is able to generate arguments motivating a given classification. C o m m u n i c a t i o n set of rules establishing α. For example, we have presented in Appendix 3 some examples of the argument (set of rules) by decision maker agent Ag1 of the classification of knowledge k2 into decision class Crucial knowledge. For more details, some examples of classification, conflicts and argument are presented in Appendix 1, 2, 3 and 4. A r g u m e n t a t i o n I n f e r e n c e m o d u l e R B i 3.2 Communication Protocol D e c i s i o n m a k e r a g e n t i C o m m u n i c a t i o n A r g u m e n t a t i o n I n f e r e n c e m o d u l e D e c i s i o n m a k e r a g e n t j C o m m u n i c a t i o n D e c i s i o n m o d u l e M e d i a t o r a g e n t 3.1 Definitions M R B j e t a - r u l e s o f c o n f l i c t r e s o l u t i o n C o l l e c t i v e k n o w l e d g e b a s e U p d a t i n g a c c e s s R e a d i n g a c c e s s Figure 1. Multiagent architecture We denote by a 1, a 2, a n decision maker agents involved in the knowledge classification process; We denote by k 1, k 2, k p knowledge to classify; We denote by K, the collective knowledge base; We denote by α, β, γ, knowledge classification; Definition 1. Classification. A classification α is represented by a triplet < a i, k, c > where a i represents the decision maker agent, k denotes the classified knowledge and c the class of classification. For example, the knowledge k2 Knowledge relative to the choice of material structure is assigned to decision class Crucial knowledge by decision maker agent Ag1 (see Appendix 4). Definition 2. Conflict. A conflict is detected iif α <a i, k, c > and β < a j, k, c > / c c. For example, the knowledge k2 is assigned to decision classes Crucial Knowledge by decision maker agent Ag1 and to decision class Non Crucial by agent Ag2 (see Appendix 4). Definition 3. Consistency. It exists Consistency iif α <a i, k, c > and β < a j, k, c >, Conflict. Definition 4. Argument. An argument is represented by a pair < α, R α > where α denotes a classification and R α the The communication protocol specifies the actions that the agents are authorized to take during the classification process. The argumentation process is initiated by the mediator agent if a conflict is detected (cf. Definition 2). A call_for_arguments message is sent by the mediator agent to the two agents in conflict which are asked to reach an agreement. After receiving this call, agents start the argumentation process. This process can be viewed as an exchange of justify messages finished by an accept or a reject message. An accept message indicates that an agreement is reached. On the other hand, a reject message implies that the mediator agent should come to an objective decision based on its meta-rules. The mediator agent algorithm is detailed in the next section. 3.3 Mediator agent algorithm The mediator agent m is responsible for solving conflicts between classifications on the basis of its meta-rules. Figure 2 shows the state graph of the mediator agent. When a conflict is detected, the mediator agent sends a Call_for_arguments message to the concerned agents and stays idle. At the end of the argumentative process, decision maker agents inform mediator agent about their decision. If the mediator agent receives an Accept, the process is complete and the classification appointed is established. On the other hand, if a Reject message is received, the mediator agent should make a decision based on its meta-rules.

4 conflict detected An agent module: responsible for representing agents by several information: its name, its strength, its associated rule base, its affectations, etc. affect(m, ai, aj,km,cn) idle Conflict resolution Metarules verification reject An argumentation module: responsible for representing, evaluating and constructing arguments; A random generation module: offers random data generation tools with respect to the definition domain (discrete, continuous); A test module: allows parameter configuration and conducting experiments. Figure 2. The mediator agent state graph 3.4 Meta-rules Table 3 represents the knowledge classification criteria and their associated rules. A meta-rule consists on giving a weight ω i to each criterion i. We choose the additive linear function as aggregation model. A classification α is then evaluated by a utility function U α : where U i is the scoring function that normalizes all criteria i to the same scale [0, 100], x α is the value of the classification α on the criterion i. Criterion Domain Description Associated rule NAg (α) [0,N] The number of agents establishing α γ(a(α)) [0,1] The approximation quality of the agent establishing α. R α [0,8] The number of rules conducting to establish α?(r α) [0,1] The average of the rules strength in R α. If NAg (α)< NAg (β) Then αp β If γ(a(α))< γ(a(β)) Then αp β If R α < R β Then αp β If?(R α)<?(r β) Then αp β Table 1. Classification criteria and associated rules 4 EXPERIMENTATIONS AND RESULTS 4.1 Experimental approach Our experimental approach is based on 2 studies: - A study based on randomly generated data: this study aims to assess the algorithmic cost of a multi-agent system implementation. Indeed, a fundamental question that arises concerns the algorithmic cost involved in the application of this approach. Thus, we measure the sensitivity of the number of unsolved conflicts implied by an increasing number of knowledge. - A study based on real data: this study aims to compare the results of human classification with the automated agents classifications. We base our experimental study on two indicators: - The concordance with the human classification results. We measure this concordance by dividing the number of concordant classifications by the number of all classifications - The sensitivity of the model that measures the impact of a small modification of the meta-rule. 4.2 Results In Figure 3, the dashed curve that represents a non argumentative approach shows a higher slope and thus a higher sensitivity of an increasing number of conflicts. We observe that a non argumentative approach is more sensitive to the variation of the number of knowledge than an argumentative approach. In this section, we evaluate the automated multiagent system in a collaborative multi-actor knowledge classification. To this end, we implemented a Java platform composed by 5 modules: A knowledge representation module: responsible for representing knowledge (name, type, content );

5 Figure 3. Impact of an increasing number of knowledge on the number of conflicts To evaluate the concordance with the human classifications, we conducted 50 series of experiments based on 50 different meta-rules. For each experiment, we measure the concordance percentage with the human classifications. Figure 4 shows that the minimal concordance percentage is %, and the maximal is 87.21%. We conclude that the results of an automated approach show a close agreement with the results of the classification of human deciders. 5. CONCLUSION Figure 5. Model Sensitivity This work details the issue of identification and evaluation of crucial knowledge and proposes an agent based argumentative approach in order to provide a conflict resolution in the context of crucial knowledge classification. The approach we have described in this paper allows agents to act in a collaborative and argumentative manner in order to classify knowledge. The aim of the proposed multiagent system is to manage conflicts between decision makers by argumentation and to lead to a consistent shared knowledge base. Figure 4. Comparison between the human classification and the automated classification To evaluate the model sensitivity, we have conducted 3 experiment series (20 similar meta-rules for each series). For each experiment, we have sensibly changed the metarule weights in order to observe whether the concordance percentage will be greatly modified. In Figure 5, we can see that for the 3 series, the concordance percentage remains insensitive to a little change in the meta-rule. In the experiments we have conducted on real data from a French car company and on randomly generated data, we observed that a non-argumentative approach is more sensitive to the variation of the number of knowledge than an argumentative one. We noted that the proposed multiagent approach presents interesting properties. Indeed, we concluded that the classification results are consistent with multiagent classifications of human decision makers in nearly 80% of studied cases. Furthermore, the sensitivity analysis of the proposed model show that the results are insensitive to minor changes of a meta-rule. In future work, we plan to conduct a comparative study of multicriteria models for the knowledge classification in order to determine which model is most appropriate to our problem. Having used the weighted sum, currently we are studying other non compensatory models such as the lexicographic model or the Chebychev model.

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