Intelligent Agent-Assisted Decision Support Systems - Semantic Scholar

Erperi Systems with Applications,

Vol. 12, No. 3, pp. 323-335, 1997 0 1997 Elsevier Science Ltd Printed in Great Britain. All tights reserved 0957-4174/97 $17.00+0.00

PII: SO957-4174(96)00103-O

Intelligent Agent-Assisted Decision Support Systems: Integration of Knowledge Discovery, Knowledge Analysis, and Group Decision Support HUAIQING

WANG

Department of Information Systems,City University of Hong Kong, Tat Chee Ave, Kowloon, Hong Kong

integration of data mining techniques with decision support systems to assist in dealing with information overload has received increased attention and importance over recent years. However, challenges remain regarding practical deployment and implementation of such integration, due to the increased complexity of decision making, system co-ordination and knowledge communication. It is the purpose of this paper to outline the issues necessary to be addressed in a practical decision support system that integrates data mining techniques. The paper will describe a novel architecture to support the co-operative decision process by utilizing event-driven and task-driven data mining agents, along with user assistant agents and a knowledge manager agent. An internet-based prototype for supporting marketing planning decisions is also presented to demonstrate the practicality and feasibility of the proposed intelligent agent-based decision support system architecture. 0 1997 Elsevier Science Ltd

Abstract-The

important contribution to the DSS community by providing techniques which future DSSs will be able to utilize in providing a wide range of information available for decision makers. The vision of the imminent integration between data mining techniques and future DSSs is compelling. An organizational vision consists of a large number of data mining agents and decision makers distributed over an organization-wide computer/telecommunication network. Each decision maker may communicate with other decision makers at other geographical locations. The decision participant may require the captured knowledge from one or more data mining agents to serve as evidence for his/her arguments, in order to aid in business decision processes. On the other hand, a number of autonomous data mining agents may be needed to continuously analyze the ever-changing organizational information source to capture meaningful knowledge, such results are then relayed to all interested parties and stored for future use. In order to fully achieve the promise of such integration of data mining techniques and DSSs, a number of limitations in today’s practical DSS systems need to be addressed before such integration can take place. Such issues include:

1. INTRODUCTION DECISIONSUPPORT SYSTEMS(DSSs) are computer-mediated tools that assist managerial decision making by presenting information and interpretations for various alternatives. Such systems can help the decision makers to make more effective and efficient decisions (Radermacher, 1994). However, recent advances in data gathering, storage and distribution technologies have far outpaced our techniques for helping today’s organiza-

tions analyze, understand and utilize this information in their decision-making processes. In this information age, useful information for decision making may be within multiple organizational databases, which in turn may be distributed over many different systems, in different forms and at different geographical locations (Abraham & Wankel, 1995). Instead of traditional DSSs, which only work with information from one or two organizational databases, in today’s ever-changing business climate, organizations are compelled to make their decisions based on knowledge captured from a wide variety of different organizational information sources. Data mining, or knowledge discovery, is an area of research that has received an increasing amount of attention in that it develops techniques and tools for the exploration of databases in an attempt to extract relevant and interesting hidden relationships that exist between variables or between causes and effects. The results emerging from the data mining community can be an

Group Decision Making: As evident from the increasing attention being paid to research into areas such as group decision support systems (GDSS) and group decision making (GDM) (Dennis et al., 1988; DeSanctis & 323

324 Gallupe, 1987), a real world decision-making process will often involve a number of decision makers who together will make the decision. The target architecture must be able to not only support the interactions between multiple decision-making agents, but also be able to facilitate each decision maker to interact with the data miners and the captured data relationships. Moreover, team-based collaboration was recently suggested as a central dimension of the networked organization (Fayyad & Uthurusamy, 1995). Multiple Data Miners: As we have articulated above, the myriad of organizational information sources require a team of data miners to distributively discover the data relationships or patterns that are contained in the information sources. The individual data miners will need to exchange the captured knowledge between themselves and make such discovered information available to the decision makers. However, it is necessary to make the distinction between task-driven and event-driven data miners. Task-driven data miners are the class of data miners whose results are only of importance to one particular decision issue at hand and are only there to provide assistance for an individual instance of the organizational decision-making process. On the other hand, event-driven data miners are miners that aim to capture data relationships with respect to some general knowledge objectives and are being executed constantly as the source data is being updated. A particular instance of event-driven data miners is the discovery of the consumption patterns of the various customer types in a department store setting, such data miners will continuously discover purchase patterns as more purchases are made. Knowledge Sharing: The interoperation between the different agents in the entire DSS framework may include the computer-mediated collaboration between the different decision makers, the application of the miner-discovered knowledge by the decision makers, the inception of a task-driven mining agent by an individual decision maker in search of knowledge or the delivery of newly discovered knowledge from mining agents to the interested parties. In order to facilitate this interthat the knowledge it is required operation, communication is interpreted in a clear, unambiguous manner (a common misconception is the term ‘above average’, with respect to differing standards) (Espinasse & Nabitz, 1993; Wang & Wang, 1996). The need for knowledge sharing gives rise to the importance of a common vocabulary amongst all the agents in the DSS. System Co-ordination: It is important for all the various agents under the DSS system architecture to be cothe overall contribute towards ordinated to decision-making process. The range of system level activities that must be co-ordinated and managed include: The initialization and execution of the eventdriven data miners, the collaborative mining effort

Huaiqing Wang between different data miners, the progress of each of the individual data miners and the interaction between each individual decision maker and their own task-driven data miners. The system will be required not only to monitor the co-operative problem solving between different system agents but also to oversee the entire lifecycle of each individual agent. The increase in attention being paid to intelligent agents and intelligent agent-based architectures in knowledge-based systems (KBS) (Tuthill, 1990) and artificial intelligence (AI) (Etzioni & Weld, 1994; Lieberman, 1995; Maes, 1994, 1995) makes it attractive for utilization in the integration of knowledge discovery and DSS. Based on our own practical involvement with numerous intelligent agent-based architectures for knowledge-based systems and intelligent control systems (Wang & Wang, 1996), the benefits of introducing intelligent agent technology into today’s DSSs to address the issues above is immediate. Our point of view regarding the usefulness of intelligent agent technology for DSSs is being shared by an increasing number of DSS researchers, including Khoong (1995) and Rao et al. (1994) in his argument for an active IDSS (Intelligent DSS) architecture. The research presented in this paper is an architectural framework for intelligent agent-assisted DSSs (IADSS). Our architecture is modeled in the spirit of an intelligent agent-based knowledge-based system such as APACS and knowledge sharing systems (Wang & Wang, 1996). The IADSS contains a set of user assistants, one for each of the decision makers in the decision-making group, as well as a set of data miners working with the vast amount of organizational data to discover the hidden data dependencies that might be of use as evidence within the group decision-making process. Incidentally, the active nature that is inherent in intelligent agents will induce active involvement in providing useful information, in contrast to the passive natural of traditional DSSs, similar to the point of view of Rao et al. (1994). Furthermore, an intemet-based prototype of the IADSS has been built using Java for applications in a real life decision support processes to demonstrate the technical feasibility of our architecture and serve as the basis for future discussion and evaluation. The rest of the paper is organized as follows. Section 2 provides a background to IADSS and related work. Section 3 presents the differing configurations of the IADSS system architecture. The description of each individual intelligent agent, their communication and coordination is presented in Section 4. Section 5 will describe our prototype implementation and application of the system for a practical domain, along with initial prototype evaluations and discussions. Finally, we shall end this paper with conclusions. 2. BACKGROUND

AND RELATED

WORK

There exist a number of research areas with results which

Intelligent Agent-Assisted Decision Support Systems

are comparable to our work in some aspects, but no directly relevant research has been found to cover all aspects. For instance, the knowledge and data discovery (KDD) community have conducted strong research into applicable mining techniques. However, little research has concentrated on integrating KDD techniques and DSS. On the other hand, the intelligent agent research community has designed and implemented numerous successful projects dealing with application domains ranging from education to project management, but little attention has been paid to the incorporation of KDD techniques. While IDSS (Intelligent Decision Support Systems) have been receiving increasing attention from the DSS research community by incorporating knowledge-based techniques to provide intelligent and active behavior, the state-of-the-art IDSS architecture provides little support for incorporating novel technologies that serve useful DSS information, such as the results from the KDD community. 2.1. Data Mining and Knowledge Discovery In recent years, the terms knowledge discovery and data mining (commonly referred to as KDD) have been used synonymously. They both refer to the area of research that draws upon data mining methods from pattern recognition (Tuzhilin, 1993), machine learning (Han et al., 1992) and database (Agrawal et al., 1993, 1994) techniques in the context of vast organizational databases. Conceptually, KDD refers to a multiple step process that can be highly interactive and iterative in the following (Fayyad & Uthurusamy, 1995): the selection, cleaning, transformation and projection of data; mining the data to extract patterns and appropriate models; evaluating and interpreting the extracted patterns to decide what constitutes ‘knowledge’; consolidating the knowledge, resolving conflicts with previously extracted knowledge; making the knowledge available for use by the interested elements within the system. A number of KDD systems are similar to IADSS data miner agents in spirit and in technique. Such systems include DBMiner (formerly DBLearn) that was developed by Han et al., at Simon Fraser University (Han et al., 1992) and Intelligent Miner, a large scale industrial strength data mining system that is currently under development at the IBM Almaden research center under the direction of Agrawal (Agrawal et al., 1993, 1994). Such work in designing and implementing practical KDD systems is crucial to our research in the sense that their results provide solid KDD pragmatic technologies ready to be integrated into our IADSS architecture. However, the current state of using KDD techniques for

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decision support remains in its infancy, as preliminary applications that use exclusively KDD techniques. It is our point of view that such isolated applications have limited scope and capabilities, while future KDD techniques will play an integral role in complex business systems that incorporate a wide range of technologies including intelligent agents, multimedia and hypermedia, distributed systems and computer networks such as the intemet, and many others. From a DSS perspective, a simple DSS architecture that consists of a single decision maker with single information source knowledge discovery functionality lacks the ability to deal with complex situations in which multiple decision makers or multiple information sources are involved. Most existing DSSs with data mining and knowledge discovery capability fall into this category. 2.2. Intelligent Agents The concept of intelligent agents is rapidly becoming an important area of research (Bhargava & Branley, 1995; Etzioni & Weld, 1994; Khoong, 1995). Informally, intelligent agents can be seen as software agents with intelligent behavior, that is, they are a combination of software agents and intelligent systems. Formally, the term agent is used to denote a software-based computer system that enjoys the following properties (Wooldridge & Jennings, 1995): Autonomy: Co-operativity:

Reactivity:

Pro-activity:

Mobility:

Agents operate without the direct intervention of humans. Agents co-operate with other agents towards the achievement of certain objectives. Agents perceive their environment and respond in a timely fashion to changes that occur. Agents do not simply act in response to their environment, they are able to exhibit goal-directed behavior by taking the initiative. Agents are able to travel through computer networks. An agent on one computer may create another agent on another computer for execution. Agents may also transport from computer to computer during execution and may carry accumulated knowledge and data with them.

Various research has been conducted into applying intelligent agent-based technology towards real world problems, including SoftBot (Etzioni & Weld, 1994), a project aimed at autonomously performing predefined general intemet tasks, developed at the University of Washington by 0. Etzioni, and Maxims (Maes, 1995; Mylopoulos et al., 1990), an intelligent user assistant for

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information filtering, as developed by P Maes at MIT MediaLab. More specifically, for example, ‘SoftBot’ (Software Robot) uses a UNIX shell and the World Wide Web to interact with a wide range of intemet resources (Etzioni & Weld, 1994). The SoftBot agent provides an integrated interface to the intemet, dynamically chooses which facilities to invoke and fluidly backtracks from one facility to another based on information collected at run time. Furthermore, there has been a rapid growth in attention paid to developing and deploying intelligent agent-based systems to tackle real world problems by taking advantage of the intelligent, autonomous and active nature of this technology (Wang & Wang, 1996).

2.3. Intelligent Decision Support Systems Intelligent decision support systems (Chi & Turban, 1995; Holtzman, 1989), incorporating knowledge-based methodology, are designed to aid the decision-making process through a set of recommendations reflecting domain expertise. Clearly, the knowledge-based methodology provides useful features for the application of domain knowledge in decision making. However, the knowledge stored in the knowledge bases is highly domain-oriented and relatively small changes in the problem domain require extensive intervention by the expert. Powerful information communication channels, such as the intemet (information superhighway), are continuously changing the decision making process. When decision makers make decisions they not only rely on brittle domain knowledge but also on other relevant information from all over the world. As a result, the challenge of discovering and incorporating new knowledge with existing ones requires us to introduce new techniques (such as intelligent agents and knowledge discovery) into DSSs. Research into IDSS includes the work by Rao et al. (1994), who presented an intelligent decision support system architecture, IDSS, that stresses active involvement of computer systems in decision making, on the other hand, the work by Sycara at CMU LEI (Laboratory for Enterprise Integration) proposed the PERSUADER (Sycara, 1993), which incorporates machine learning for intelligent support of conflict resolution and the work on NEST which incorporates distributed artificial intelligence (DAI) with group decision support systems by Fox and Shaw (Shaw & Fox, 1993). The proposed IDSS architecture is similar in substance to our proposed IADSS, which incorporates distributed artificial intelligence and incorporates the principles of co-operative distributed problem solving in the decision-making process. However, as we have pointed out above, it is necessary for the incorporation of data mining technology which extracts important information from vast amounts of organizational data sources in order to provide additional information that may be crucial for the decision-making process.

Huaiqing Wang 3. IADSS ARCHITECTURAL CONFIGURATIONS As we have pointed out in our introduction, there exist numerous obstacles that remain to be overcome in today’s DSSs to fully achieve the vision of IADSS. The integration of intelligent agents with DSSs will be able to address most, if not all, of the articulated issues. However, even within the application of an intelligent agent-based architecture, there exists two different forms of the decision-making process that (or conjgurations) the particular architecture will be able support: Single decision maker-multiple miners and multiple decision makers-multiple miners.

3.1. Single Decision Maker-Multiple Miner DSS Processes We have argued in the previous section that a possible configuration of IADSS architecture, namely the single decision maker-single miner form, has severe limitations when it comes to extendibility and the ability to be integrated into an overall organizational decision support framework. However, in many real life cases, the single decision maker situation is still of importance. In today’s organization, there may exist a myriad of organizational information sources on which useful data relationships and patterns may be discovered to support the singular decision maker’s decision process. As a result, the IADSS configuration of a single decision maker with multiple data miners warrants attention and analysis. Under IADSS, the architecture of such a single decision maker, multiple knowledge miners assisted DSS is shown in Fig. 1. There are three classes of intelligent

System

FIGURE 1. A multi-agent-based DSS.

Intelligent Agent-Assisted Decision Support Systems

agents (we call them decision support agents or DS agents) contained within this architecture: Knowledge miners that discover hidden data relations in information sources, user assistants that act as the intelligent interface agents between the decision maker and the IADSS and a knowledge manager with repository support that provides system co-ordination and facilitates knowledge communication. Further details about the functionality and internal structure about each type of agent is elaborated in the next section. 3.2. Multiple Decision Maker-Multiple Assisted GDSS Process

Miner-

The single decision maker configuration discussed above can be easily extended into a group decision support system (GDSS) architecture (as seen in Fig. 2 by the introduction of additional user assistants for each additional decision maker). Compared to the single decision maker configuration in Fig. 1, each user assistant agent is further augmented to provide support for group-based communication between different decision makers. It is important to observe that with the introduction of each additional DS agent, only an extra knowledge communication channel between the new DS agent and the knowledge manager is needed. This enables a manageable linear increase in the number of knowledge communication links corresponding to the increase in the number of agents in the IADSS system, rather than the quadratic increase in the number of direct communication links in a direct agent-to-agent fashion. Furthermore, our proposed IADSS is an open archi-

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tecture with potential for the integration of future technologies by the incorporation of additional classes of intelligent agents. 4. IADSS ARCHITECTURE 4.1. Intelligent

AT A GLANCE

Decision Support Agents

As described above, there are three types of intelligent agents in an IADSS system: Knowledge miners, user assistants and knowledge managers. This section will provide a more detailed description of such agents and their internal architectures. 4.1.1. Knowledge Miners. The role of knowledge miners in IADSS is to actively discover patterns or models about a particular topic which provides support in the decision-making process. The architecture of a knowledge miner is shown in Fig. 3. There are four components in a knowledge miner. The IADSS interface component manages the communication between the miner and the knowledge manager. When a knowledge miner receives messages that are represented in a common representation, the IADSS interface translates these messages into the local format based on the common vocabulary. On the other hand, when the knowledge miner sends messages out, the IADSS interface translates them into common format first, then sends them to the knowledge manager. In order to carry out the mining task, the necessary control knowledge as

/

A Knowledge

Miner

*

Information Source B

Collaboration

Common

FIGURE 2. A multi-agent-based GDSS.

Vocabulary

FIGURE 3. The architecture of a knowledge miner.

328 well as domain knowledge is kept in the knowledge base component, while the data interface component serves as a gateway to the external information sources. The knowledge discovery is usually done by discovering special patterns of the data, i.e. by clustering together data that share certain common properties. For instance, a knowledge miner may find that within this week, a number of stocks are going up. There are two different types of knowledge mining agents, event-driven knowledge miners and tusk-driven knowledge miners. The event-driven knowledge miners are agents that are invisible to the decision makers, and their results may contribute towards the decision-making process. Based on the specification of the IADSS, such event-driven knowledge miners start when the IADSS starts up. When a particular event comes, an agent will start its knowledge mining. Events may be temporal events, e.g. every day at 1 a.m., every hour, etc. Or, events may be constraint-triggered events, e.g. every 10,000 customers, when a certain type of customer reaches lo%, etc. Usually, such event-driven knowledge miners work periodically. They follow a sleep-work-sleep-work cycle and will be destroyed when the entire IADSS system terminates. On the other hand, task-driven knowledge miners are created for particular data mining tasks based on requests originated by the decision makers. After a knowledge miner completes its task, it sends the mining results to the knowledge manager and is then terminated automatically. From the view point of decision support, knowledge miners play the role of information extractors which discover hidden relationships, dependencies and patterns within the database, whether the information is discovered by an event-driven knowledge miner or a task-driven knowledge miner, which may be utilized as evidence by decision makers within the GDM process. 4.1.2. User Assistants. Interaction between a particular decision maker and the IADSS is accomplished through an user assistant agent. The architecture of a user assistant contains four components, as shown in Fig. 4. The multimedia user interface component manages the interactions with the decision maker such as accepting requests for a task-driven knowledge miner, while the IADSS interface manages the knowledge communication with the knowledge manager. The necessary knowledge such as the common vocabulary, decision history and others are kept in a local knowledge base component. All three components are controlled by an operational component that provides the facility of inferencing, multimedia presentation and collaboration. With regard to the role the user assistant plays in the decision process, it enables the decision maker to view the current state of the decision process and to convey his or her own opinions and arguments to the rest of the decision making group. It also enables the decision

Huaiqing Wang

A User Assistant

Decision Maker John

Operational

Facilities

‘mpi

L /

Agent Knowledge

Common

Base

\

Vocabulary

FIGURE 4. The architecture of a user assistant.

maker to issue requests for task-driven knowledge miners to attempt to discover some particular type of organizational knowledge from business data. The user assistant will then relay the request to the knowledge manager and interpret the mining result if it is deemed appropriate. 4.1.3. Knowledge Managel: The knowledge manager provides management and co-ordination control functions over all the agents in the IADSS architecture. The internal component-wide architecture of the knowledge manager is shown in Fig. 5 which contains four components: The decision maker interface, the operational facilities, the miner interface and the agent knowledge base that provides support for localized reasoning. From the functional standpoint, the knowledge manager provides the following functionality in the IADSS architecture:

(1) Makes decisions concerning

(2)

the creation and termination of knowledge miners as provided by the miner interface component of the knowledge manager. Mediates requests from user assistants through the decision maker interface, analyzes these requests through the localized knowledge and inferencing engine and then initiates an appropriate group of task-driven knowledge miners to collaboratively perform the requested task through the miner

Intelligent Agent-Assisted Decision Support Systems

329 4.2. IADSS Repository for Knowledge Sharing

0 \I User , 0 User Assistant

I

A Knowledge

Decision Maker

Manager

I

0 0

Assistant

m

FIGURE 5. The architecture of a knowledge manager.

(3)

(4)

(5)

(6)

interface. Mediates the discovered knowledge from knowledge miners (whether it is an event-driven or a task-driven miner), stores the knowledge into the repository for possible future usage and forwards the relevant knowledge to interested decision maker users through the decision-maker interface. Manages and co-ordinates the knowledge transactions with each individual decision support agent such as common vocabulary, available decision topics, existing mining results and strategic knowledge, as provided by the operational facilities component. Manages the synchronization between the collection of decision support agents such as the progress of the task-driven knowledge miners and the notification of the decision makers when crucial knowledge is discovered. Mediates all other types of communication among decision support agents including the communication among user assistants and supports the retrieval of appropriate evidence from the repository by user assistants.

In terms of the decision support process, the knowledge manager plays the role of manager and mediator between two decision makers, between the decision maker and the corresponding task-driven miners and between all decision support agents and the repository to address the issue of knowledge sharing.

In the IADSS, the repository provides the means for the common vocabuh-y. As a result, information relevant to the management, evolution, operation and maintenance of the IADSS can be shared in a transparent fashion. The repository also provides management for all schema evolution transactions. Such transactions can be initialized by application programs, or be triggered by events, updates, time conditions, etc. As the repository under the IADSS framework supplies generic software and knowledge, the construction of the IADSS system for different application domains becomes a matter of specifying the components and topology of the target domain rather than a fullyfledged domain model engineering effort. As all the components work on the same problem domain and communicate with a set of valid message objects, it is essential for all IADSS components to share a common vocabulary. Furthermore, meta-information stored in the repository such as the system configurations (e.g. knowledge miner mining parameters), the design considerations of the IADSS framework (i.e. generic software and knowledge) can be shared and reused by other similar systems in the future. 4.3. IADSS Ontology An ontology is a formal description of entities and their properties; it forms a shared terminology for the objects of interest in a domain, along with definitions for the meaning of each of the terms (Gruber, 1994). The basic problem of ontologies is the development of a sufficient presentation notation with which to represent knowledge. The knowledge representation scheme to be used to model IADSSs is based on the knowledge representation language Telos, developed at the University of Toronto (Mylopoulos et al., 1990). The Telos knowledge representation language adopts a representational framework which includes structuring mechanisms analogous to those offered by semantic networks and semantic data models namely classification (inverse instantiation), aggregation (inverse decomposition) and generalization (inverse specialization). Furthermore, Telos also supports the additional representation mechanisms of temporal knowledge and assertions. The Telos knowledge representation scheme is used to represent and model the variety of types of knowledge required by an IADSS within a consistent framework. The ontologies for IADSSs contain various basic entities which are represented as objects with specific properties and relations. Objects are then in turn organized into taxonomies. As an example, the concept of agent in IADSS is presented by a hierarchy of Telos concepts. At the metaclass level, there is a meta-class AgentClass, which has an instance DSAgent, that is a domain-level class. DSAgent in turn has three subclasses, Knowledge-

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Manager, KnowledgeMiner and UserAssistant, corresponding to the three types of DS agents under IADSS. Furthermore, KnowledgeMiner has two specialization sub-classes, EventDrivenMiner and TaskDrivenMiner. An actual knowledge miner is modeled as an instance (Token) of them. 5. IADSS PROTOTYPE

AND APPLICATION

This section describes an IADSS prototype design and implementation based on our architecture framework as discussed earlier, as well as application of the prototype towards marketing planning decision support. However, it should be noted that the marketing application domain is only used to demonstrate the technical feasibility of our IADSS architecture as well as serve as a test bed for future discussions. The IADSS architecture can be applied to a wide range of application domains ranging from crisis management (DeSanctis & Gallupe, 1987) to market share analysis (Ghose & Nazareth, 1994).

5.1. Marketing Decision Support Scenario Given the increased sophistication and availability of computing resources, the accessibility of large databases and the competitive pressures of modem businesses, the need for automated support for marketing decisions has grown considerably (Ghose & Nazareth, 1994). Such marketing decision support systems (MDSSs) enable marketing managers to incorporate the state-of-the-art information technology into marketing decision processes. Within our prototype, we assume that the decision-making scenario involves a number of marketing managers in a marketing department who need to select their promotion types for their products. There are a number of possible marketing channels, including TV, newspaper, intemet, magazine, etc. Such a marketing planning decision is usually based on market segmentation and sale potential assessment. We may further assume that there are a number of business information sources that may provide important information for marketing planning. For instance, the company has a database that stores all the intemet accesses for browsing their products. The company has also a sales database storing information about any sales transaction. In addition to the company’s own databases, there are a number of information sources outside the company. For example, a number of intemet-based companies (e.g. NECX, OnSale, UCE, etc.) may be able to provide intemet traffic databases; some market analysis companies (e.g. DataQuest) may provide unstructured information sources, such as documents, tables, etc. 5.2. AMDSS (Agent-Assisted MDSS) Based on the scenario described above, AMDSS, a prototype Agent-assisted Marketing Decision Support System, has been designed and implemented. The

architectural design of AMDSS is shown in Fig. 6, and is based on our novel architecture in Section 3. The AMDSS is implemented on two computers, both are SUN Spare workstations. The knowledge manager and the repository are on one SUN, called the host machine. There is a customer access database on the host machine, storing the customer’s access records via the intemet. The sales database, on the other hand, is stored on the other Sun workstation at another geographical location. The knowledge manager in AMDSS is implemented using Java (a platform-independent object-oriented language available from Sun MicrosystemsTM that has recently gained tremendous popularity and attention). The repository in AMDSS is constructed as a DBMS application. Both of them are on the host machine. There are two major components in the repository: A database and a database gateway written in Java using JDBC (Java DataBase Connectivity, newly developed by Sun Microsystems as a database extension package to Java). JDBC provides basic SQL functionality to access databases. JDBC has been adopted by nearly all the database vendors as their intemet interface. Through JDBC, the knowledge manager is able to query and update the repository. The knowledge manager requires that a knowledge miner co-ordinator be contained in each machine that

. . .

Intelligent Agent-Assisted Decision Support Systems

houses potential databases to be mined. There are two knowledge miner co-ordinators in the AMDSS for two computers. The knowledge miner co-ordinators are implemented in Java and Java RMI(TM) (Remote Method Invocation). Java RMI allows the communication between two Java applications, which could be at different locations and interconnected via the internet. When the knowledge manager needs to create a knowledge miner, it sends requests to a corresponding co-ordinator and the co-ordinator will create a knowlSimilarly, a edge miner on its local machine. co-ordinator can destroy a knowledge miner based on a request from the knowledge manager. Knowledge miners in AMDSS are implemented using Java and MLC++ (Machine Learning C++), a library provided by SGI. The Java part in a knowledge miner is implemented on the top of RMI and JDBC. The RMI manages the communication to the knowledge manager, while the JDBC is the interface to the database containing useful information to be mined. When a knowledge miner completes its task, the results are passed to the Java interface and then transferred to the knowledge manager through Java RMI. User assistants are implemented using Java and HTML. The communication between a user assistant and the knowledge manager is through Java RMI. A decision maker can join the AMDSS through a Netscape browser. Using a Netscape browser, the user is able to download the user assistant software (Java and Java RMI binary files) from the host machine. The user assistant software is then executed on the local environment.

5.3. Marketing

Decision Support Process

The specific marketing planning decision support process as provided by AMDSS is further decomposed into the following steps:

(1) Start the AMDSS, i.e. start the knowledge manager, and the repository on the host machine. After the knowledge manager has started, it is connected to the repository and retrieves the necessary knowledge (e.g. the definition of UserAssistant, common vocabulary, etc.) from the repository. (2) In our example, the knowledge manager starts two knowledge miner co-ordinators, one of which is on the host machine and the other co-ordinator is on another computer which contains the sales database. Then the connections between the knowledge manager and the co-ordinators are set up. (3) Start event-driven knowledge miners. Based on the system configuration, the knowledge manager sends messages to the knowledge miner coordinators to ask them to create event-driven knowledge miners. (4) When a decision maker wants to join the AMDSS, he/she should ask the group leader to create an

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account (with an initial password). When a decision maker logs in via Netscape or another intemetbased browser (e.g. HotJava), the user assistant software is downloaded from the host machine and runs on the local machine. (5) The user assistant displays an interface window to the user (Fig. 7). The left part of the interface window shows the decision topic hierarchy. For instance, the top level topic in this specific decision-making process is Marketing Strategy, which has five sub-topics. Each topic may have its own contents (i.e. comments or arguments written by decision makers). The open folder indicates that the current displayed topic is intemet marketing. When a topic has been updated, the ‘attention’ icon is then added to the corresponding folder, e.g. the topic Newspaper Ads in Fig. 7. The decision maker is able to click on any topic to view its content. The right part of Fig. 7 shows the contents about the decision topic intemet marketing. There are two comments about this topic. One is by Mark and the other is by an anonymous user. The decision maker can create a sub-topic of the current topic by clicking on the Create Sub-topic button. (6) Decision makers may want to discover specific business knowledge by requesting for task-driven knowledge miners. Such a request would be initiated by clicking on the Task Miner button at the bottom left button in Fig. 7. After clicking, a taskdriven miner set-up window pops up (see Fig. 8). The window enables the user to select an independent variable, a set of dependent variables and a set of variable constraints. After selection, the user can click on the Submit button to submit the request to the knowledge manager. (7) When the knowledge manager receives the mining requests, it first queries the repository to see if knowledge pertaining to the request has already been discovered (for example, by some eventdriven knowledge miners). If such knowledge is not found, the knowledge manager then initiates one or more knowledge miners through the corresponding knowledge miner co-ordinators to mine the appropriate databases. (8) Task-driven knowledge miners do knowledge discovery. Based on the tasks specified by the knowledge manager, knowledge miners query their information sources, conduct data-mining and report conclusions to the knowledge manager. (9) Upon receiving reports from knowledge miners, the knowledge manager stores them in the repository and sends signals to corresponding user assistants. Decision makers are able to browse discovered (10) knowledge from knowledge miners (see bottomright portion of Fig. 9). The user is able to make comments on a topic based on such evidence. The top-right in Fig. 9 shows a comment editor. The

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Huaiqing Wang

user can attach any evidence to his/her comment by clicking on the corresponding button. For instance, evidence-l has been attached to the comment already. When the user finishes the Comment Editor, he/she can choose either signed-submit or anonymous-submit. Such submission will pass the comment to the knowledge manager and the comment will be stored in the repository. Such signals (i.e. the new comment) will be passed to all the users.

5.4. Prototype Evaluation As AMDSS has been designed to provide a more active group decision support for marketing planning, we have organized a small group of subjects to conduct several experimental decision-making processes. A typical experiment includes a number of subjects at different geographical locations. For example, some of them are in their offices and others are at home. We have also arranged for some subjects to try AMDSS during the day time, while others try it in the evening. Furthermore,

some of them may use UNIX workstations (e.g. SUN workstations and SGI), while others may use IBM PCs. The minimum request for participating in such an experiment is to have Netscape (version 2.02 or above). Based on discussion with subjects afterwards, we have summarized the following comments: It is very helpful that AMDSS is able to discover useful information automatically. AMDSS is very flexible. Users can join a group decision-making process any time, at any location and on any type of computer. Users do not need to install any particular software or network to run the system. The evidence browser and the evidence attachment facility help decision makers to resolve different opinions more easily. AMDSS is very user friendly with a high degree of interaction. The multimedia-based evidence browser is very helpful for decision makers to make decisions. The performance of AMDSS is slow sometimes, e.g. when a user starts-up, or when the user is browsing evidence. The main reason for the slowness is due to heavy traffic on the intemet.

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When there is too much evidence, it is very difficult to find relevant evidence for a particular intention. It is necessary to have an evidence classifier to organize evidence in a structured fashion.

GDSS framework by means of applying intelligent agent technology. Under IADSS, three distinct types of decision support agents have been identified. The knowledge miners extract patterns and models from information sources. User assistants serve as enablers that facilitate the knowledge interaction between the particular decision makers and the IADSS system. The knowledge manager plays the role of manager, coordinator and knowledge mediator that controls the knowledge flow between different decision support agents in IADSS. Under the IADSS, the knowledge miner agents can discover the hidden relationships and dependencies from a vast ocean of organizational data for the support of arguments in the decision-making process. Furthermore, the distinction was made between task-driven knowledge miners that attempt to discover particular knowledge that is useful for a specific objective and event-driven knowledge miners that continuously discover a wide range of knowledge that is of utility in supporting different arguments. Active intelligent agent-supported decision-making processes. The agents can understand the intentions and objectives of the particular decision maker and act

6. CONCLUSIONS There is currently a growing sense of awareness in the DSS community about the importance of integrating KDD techniques into existing DSS to utilize discovered hidden data dependencies and relationships in the overall decision-making process. This paper has identified the various issues, group decision making, multiple data miners, knowledge sharing and system co-ordination that are central to such integration of two vastly different technologies. We have then proposed an intelligent agent-based novel architecture, IADSS, that takes advantage of the intelligent, autonomous and active aspects of intelligent agent technology. In particular, the IADSS architecture has the following novel features: ??

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