ous decisions. Applying decision rules based on notation/terminology used in an or-ganisation allows making decisions in dispersed environment where a decision maker elaborates a description of a decision situation using the natural language. The conclusions of the papers presents a SWOT analysis of applying the proposed solution in enterprises.
Keywords: MCDA, decision aiding, software agents 1. Course of the decision process
Roy [1] defines decision aiding as taking actions according to the resultant influence of a De-cision Maker’s (DM) value system, where the DM is a single person or a committee. The deDe-cision process includes a set of activities grouped at four levels as follows:
1. determining criteria of identifying possible actions and the goal of decision making, 2. consequence analysis and modelling judgements,
3. identifying global preferences and techniques of judgements aggregation, 4. choosing a decision problematic and creating a recommendation.
Applying a formalised decision approach requires to align the structure of the description of a decision situation made by a DM, to a form which fits to a chosen methodology. Adapting ac-tions are performed as an analytical task, where an analyst moderates a dialog with a DM. An ana-lyst uses his domain knowledge along with the technical knowledge about courses of the decision process with various techniques. The dialog is lead in a direction which allows further building a decision model according to the requirements of a selected process. The Source: based on [2]
Fig. 1 The course of the decision process Source: based on [2]
tives is done in the same location as a data source. Such an approach is based on an assumption that the client-server paradigm is replaced by a more efficient approach. All computations are done in the location where data are stored. The advantage of such an approach is transmitting only re-sults or performance values, in case of a decision agent. Hence, the load of transmission channels is lower. Additional advantage of using an autonomous entity (with its own goals) is the ability of independent searching and discovering data sources [4].
The paper [5] presents a decision aiding agent, which decomposes the decision process into components. Moreover, a decision problem is divided into subproblems, which are assigned to different agents. Namely it is a multi-agent system. The described solution for aiding decisions is designed to solve a fixed structure of decision problems with varied input data. Decomposition of a decision problem into specific task uses expert knowledge.
3. Issues of achieving mobility in decision aiding
Analysing a decision situation allows to isolate a spectrum of consequences, which is repre-sented as a set of dimension of individual actions. Choosing an appropriate level of completeness when creating a spectrum of consequences is an analytical task, which cannot be automated [1].
Analytic activities are done by a decision maker and an analyst cooperating as a team. A DM elaborates his view on a decision situation, then an analyst adds his knowledge about formalisation techniques which can be applied in a given context of a decision process.
Structuring the description of a decision situation requires not only the knowledge about a si-tuation itself, but also about its environment and considered alternatives. Therefore, a dispersed decision process, where formulating a problem is preceded by a time consuming process of gather-ing decision alternatives (a searchgather-ing various physical data sources, acquirgather-ing proposals from busi-ness partners) requires involving an analyst in the process at least twice.
The outcome of an analyst’s assistance is a specification of informational requirements and re-jecting criteria for decision alternatives. Formalised form of a decision problem allows to search decision alternatives which meet minimal requirements for implementing them. Automating this stage of the process is a very difficult task, due to the unstructured description of a problem pre-sented by a DM. The difficulty is a result of the fact that usually a DM is not an expert in knowl-edge modelling.
the process is the result of gathering decision alternatives from various locations. Lack of ability to estimate the time required to build a complete set of decision alternatives makes designing an effi-cient process difficult. Furthermore, this is a reason why an idle phase comes after identifying all decision alternatives (and the decision process takes more time), where the process has to be sus-pended until consultation with an analyst is possible. Additional time of the decision process de-pends on an analyst’s availability, and can bring additional costs for a decision maker (cost of im-proving availability of an analyst by offering him additional remuneration).
The second task for an analyst is to use information provided by a DM together with descrip-tions of decision alternatives to choose the best multicriteria technique to transform gathered jud-gements into a preference system which allows to identify mutual relations between considered alternatives. In the last phase of the process, the identified global preferences allow to create a rec-ommendation according to a decision maker’s request (decision problematic).
4. Suggested expert system analysing a decision situation
An approach for building a software agent presented in [5] is based on expert knowledge about a particular decision situation, which makes applying the agent limited to situations covered by the knowledge base. However, aiding decisions where there is no knowledge base, requires con-sultation with an analyst to build a decision model. Hence, the mobile approach in situations, where the input set is not known a priori is limited. Inability to early determine all premises of a decision situation makes consultation with an analyst inefficient.
As a solution addressing the issue introduced in the previous paragraph an expert system is proposed. The novelty of the proposed system is analysing the meta-data layer of the description of a decision situation. The task of analysing a situation was presented in the introduction of the pa-per. Such a task requires consultation with a decision maker, followed by synthesis: gathered opin-ions, input data of the process and experience of an expert (analyst). The model automating the mentioned process is given on the Fig. 2.
Fig. 2 The expert system analysing a decision situation
The analysis process presented on the Fig. 2. assumes using an expert system and an expert knowledge base. The strength of such a system depends strongly on completeness of the knowl-edge base. The proposed expert system evaluates a given decision situation based on the descrip-tion provided by a DM and it considers decision aiding methods (multicriteria methods) which are included in a set of methods implemented in the agent’s code. The agent’s work requires transfer-ring its code to multiple hosts in order to search for decision alternatives which fulfil DM’s re-quirements (potential alternatives). Descriptions of decision alternatives as well as characteristics of data sources consist partially of meta data of the process. The complete definition of a problem along with the meta-description of the context of a decision situation are used by the agent for in reasoning. The outcome of the reasoning process is a decision model built with DM’s data, gath-ered input data and the structure resulted from an activated subset of decision rules. Afterwards, the decision model is solved and the results are used to make a recommendation for a DM.
5. Creating rules based on historical data
The phase of building the knowledge base requires participation of an analyst which goes be-yond only aiding a DM. The assistance of an analyst needs to be extended to make use of provided information not only according to the conventional meaning of the process. Hence, knowledge gain from an analyst’s assistance should be utilised as a source of a process of supervised learning of a software agent. Building a knowledge based is based on the description of a decision situation, which consist of the two main dimensions:
• dimension of the definition of a decision situation (decision problem),
• dimension of the meta-description of a decision situation (environmental context).
his-torical knowledge from decisions made in the past. It is crucial to include the context of previous decisions by tracking progress of the dialog between an analyst and a DM. The data gathered in such interaction would allow to create rules which can be used when solving future decision situa-tions in similar contexts.
Giving to much autonomy to an agent witch its own set of rules can arise a threat of making unwanted decisions. Such a situation may happen when incorrect rules are activated. It may happen because people don’t follow a one schema which can be encoded once. Moreover, changes in a structure of data gathered from sources of decision information may occur. Each change of the data structure creates a requirement of rebuilding data gathering modules [6].
6. Concluding remarks
Filling the knowledge base gives an opportunity to use the meta-description of a decision situation gathered during interaction with a DM. The dialog leading to define a decision problem (user interface functionality, a program to set up an agent) still requires participation of an analyst when structuring informal form of the description of a decision situation elaborated by a DM. Nonetheless, the context information, in accordance to the structure of rules included into the knowledge base, makes possible to initiate the reasoning process after obtaining a satisfactory (complete) set of descriptions of decision alternatives. The completed reasoning process results in choosing an approach suitable for aiding the decision in a given situation. Choosing a method along with the aiding process itself are done during the autonomous phase of the agent’s operation. Hence, only a single interaction with an analyst is required. Gathering decision alternatives de-scriptions, choosing an appropriate aiding technique, computations of the decision process result-ing and finally drawresult-ing a recommendation are all automatic phases of the process.
Analysis of historic data requires identifying dependencies between data. As a method of identifying dependencies, the frequent sets analysis, along with associations discovery using Apri-ori algApri-orithm [7] were chosen. The APRIORI software [8], which was created and is developed at the University of Magdeburg was used. The algorithm of the mentioned software is included in the complex data-mining solution SPSS Clementine.
Sample frequent sets which were discovered when analysed chosen positions from scientific literature are presented below. The numbers in parentheses are the support ratio and the absolute number of occurencies of the set.
Sample frequent sets:
• Method_1 K5_0 K8_0 K6_0 K3_1 K4_1 (14.3/3) • K7_1 K9_0 K5_0 K8_0 K3_1 K4_1 (19.0/4) • K9_0 K5_0 K8_0 K3_1 K4_1 (28.6/6) • K9_0 K5_0 K6_0 K3_1 K4_1 (28.6/6) • Method_1 K7_1 K9_0 K8_0 K4_1 (14.3/3) • Method_1 K7_1 K9_0 K6_0 K4_1 (14.3/3) • Method_1 K5_0 K6_0 K3_1 (19.0/4) • Method_1 K5_0 K6_0 K4_1 (28.6/6) • K4_0 K3_1 (19.0/4) • Method_3 K7_2 (14.3/3)
deter-decision process with an analyst’s schedule (the availability issue) results in time savings and cost cuts of the process. The Table 1 presents an analysis of applying the proposed approach in an eco-nomical organisation (an enterprise). The results of the analysis is presented as the SWOT table.
Table 1 SWOT analysis of the agent for aiding decisions using context information Strengths
• Lack of the necessity of waiting for a sec-ond interaction with an analyst
• Automated choice of a multicriteria method allows to autonomous solving of a decision model
• Possibility of performing calculations in a data source
Weaknesses
• Reasoning is reliable only for situations described in the knowledge base
• Assistance of an analyst is still required in formalising the description of a decision situation
• Requires structured form of descriptions of decision alternatives
Opportunities
• Text-mining analysis of documents accom-panying the decision process would allow discovering decision alternatives from un-structured sources
• Dispersed information sources would gain benefits from applying software agents • Formalised structure of management
in-formation in enterprises (ISO norms, widely applied data warehouses) allow to use existing information structures when defining decision situations (lower re-quirement for an analyst’s assistance)
Threats
• Data used to build the knowledge base may not fit to real operations of an organisation • Changes of the environment can depreciate
the knowledge base
The described approach allows to automate the task of choosing a multicriteria approach suit-able for a given decision situation. Despite automating only a part of the decision process, such a task gives significant benefits in the time and the cost of the decision process.
7. Literature
1. B. Roy, Wielokryterialne wspomaganie decyzji, Wydawnictwa Naukowo-Techniczne, Warszawa, 1990.
2. Y. Siskos, E. Grigoroudis and N. F. Matsatsinis, UTA Methods, Multiple Criteria Deci-sion Analysis: State of the Art Surveys, 2005.
3. S. Franklin and A. Graesser, Is It an agent, or just a program?: A taxonomy for autono-mous agents, Intelligent Agents III Agent Theories, Architectures, and Languages, 1997, pp. 21-35.
4. P. Vu Anh and A. Karmouch, Mobile software agents: an overview, Communications Magazine, IEEE, 36 (1998), pp. 26-37.
5. T. Bui and J. Lee, An agent-based framework for building decision support systems, De-cision Support Systems, 25 (1999), pp. 225-237.
6. H. Chalupsky, Y. Gil, C. A. Knoblock, K. Lerman, J. Oh, D. V. Pynadath, T. A. Russ and M. Tambe, Electric Elves: Applying Agent Technology to Support Human Organizations, Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelli-gence Conference, AAAI Press, 2001, pp. 51-58.
7. C. Borgelt and R. Kruse, Induction of association rules: Apriori implementation, Proceed-ings of the 15th Conference on Computational Statistics (2002), pp. 395?400-395?400. 8. http://www.borgelt.net/apriori.html.
Ryszard Budziski Zbigniew Piotrowski Jarosław Wtróbski
Instytut Systemów Informatycznych Wydział Informatyki
Politechnika Szczeciska
71-210 Szczecin, ul. ołnierska 49 e-mail: rbudzinski@wi.ps.pl e-mail: zpiotrowski@wi.ps.pl e-mail: jwatrobski@wi.ps.pl
