DSS AGENTS AND MULTI-AGENTS

13.8 DSS AGENTS AND MULTI-AGENTS

DSS AGENTS Some of the agents described earlier can be classified as problem-solving or DSS

agents (see Kvarroov.com). A framework for DSS agents has been proposed by Hess et al. (2000), who distinguish five types: data monitoring, data gathering, modeling,

TABLE 13.1 Example Agents IJtili/ed in the Extension of the Holsapplc and Whinston (19%) Manufacturing Firm DSS. Agent Agent Essential Characteristics Reference Point

Autonomous Homeostatic

Employer/

Domain Data monitoring

Report when any

Monitor the current Vendor site on an price change

Stay at supplier's

Capable of

User

rates of the three extranet crosses given

site "forever" or

detecting vendor

types of resources threshold values

as long as the

price changes

vendor supplies

and report on

parts

them

Data gathering Report discovery

Look for alternate Travel to directory of potential

Lifetime of the DSS Capable of

User

examining

vendors of

suppliers of

specific part; if manufactured

directory sites

and

found, send

message back reasonable prices

parts at

understanding

language used

with name and

location of source Modeling

there

Maintain "optimal" Lifetime of the DSS Capable of receiving Domain manager When notified by Model base price and resource

D M A , formulate management policies; report

inputs from the

agent ( D M A )

an LP model, system (MBMS) significant dollar

domain manager

of DSS consequences

agent ( D M A ) and

solve it using

passing results

Excel's solver,

back to the D M A

and report solution to D M A

Domain managing Monitor location

Monitor all other Database (say, in the

Lifetime of the DSS Capable of

User

agents (both local management DBMS)

and tasks of both

communicating

local and remote

system (DBMS) agents functioning

with agents (even

and remote)

acting on behalf of DSS (similar on behalf of

at a distance) and

of the domain; agents exist in domain activities;

keeping track of

the MBMS and respond to all

their whereabouts

trigger

DGMS) messages.

appropriate

actions on hearing from them

Preference Learn a specific

Record whether Dialog generation learning

"Lifetime" of a user

Capable of

User

and based on the

user's preferences

of the DSS, even

observing user

specific user

takes modeling management actual history of

across different

actions and

agent's advice or system (DGMS) user/DSS

sessions

storing them

proceeds on own of D S S interactions

C H A P T E R 13 INTELLIGENT SYSTEMS OVER THE INTERNET

three major characteristics and three reference points. This table, based on the work of Holsapple and Whinston (1996), presents examples from a manufacturing firm DSS.

Furthermore, Hess et al. have proposed a general framework in which they map the five types of agents against the three major components of DSS (data, modeling, user interface). This framework is shown in Figure 13.6, and it can be used as a guide in agent development and research.

MULTI-AGENTS

Multi-agent systems are a computer-based environment that contains multiple soft- ware agents to perform certain tasks. The theoretical basis for multiple agents started with research in a field called distributed artificial intelligence (DAI), which basically represents the intelligent part of distributed problem-solving. D A I is the study of dis-

tributed but centrally designed Al systems (Avouris and Gasser, 1992) and involves the design of a multiple-agent distributed system with a problem to solve or a task to accomplish. The issue is how to perform in an effective and efficient manner.

FIGURE

1 3 . 6 T H E AGENT-ENHANCED GENERAL D S S FRAMEWORK

p S L Stand- i*

M BM S Modeling Packages

Alone

/DMA\

Metalmodeling

C T f c > Modeling AgunLs L j Proxy Agent

' •••' Proxy Agent Prfjferenc.fi Preference

Learning Agent f , L e a r n i n g Agent

Contact Agent

P A R T IV INTELLIGENT DECISION SUPPORT SYSTEMS

The D A I approach decomposes the task into subtasks, each of which is addressed by an agent. Therefore, in distributed problem-solving it is assumed that there is a sin- gle body that is able, at design time, to directly influence the preferences of all the agents in the system ( O ' H a r e and Jennings, 1996). The infrastructure of D A I can be constructed with an architecture known as a blackboard (Avouris and Gasser, 1992). Nute et al. (1995) provide an example of how to perform forest management with a blackboard architecture written in P R O L O G . Shih and Srihari (1995) describe D A I in manufacturing-system control.

However, distributed artificial intelligence systems differ f r o m multi-agent sys- tems. There is no single designer standing behind all of the agents in a multi-agent sys- tem. The agents can be working toward different goals, even contradictory goals, and sometimes in parallel; they can cooperate or compete with each other (Decker et al.,

1999). In a D A I system, an agent acting in a particular way is good for the system as a whole, which is not necessarily the case in a multi-agent system. However, by using incentives, it is possible to influence the agents in a multi-agent system. For example, Chi and Turban (1995) proposed a DAI system for an EIS. Wang et al. (1996) define a model of an autonomous agent in a multi-agent environment, focusing on belief-state models of the agents and the changes that communication forces on their belief states. Chau et al. (2002) used the multi-agent approach to Web mining. O ' H a r e and O ' G r a d y (2003) proposed a multi-agent system for intelligent content delivery. This should lead to better communication so that the agents can solve a problem cooperatively in a dis- tributed open system. The agent environment is called a multi-agent processing envi- ronment (MAPE). Figure 13.7 shows an example of a multi-agent system architecture called Genie, in which the identification agent identifies p r o p e r user, the calendar agent schedules events, and the Web agent interacts with the user through Web-based user interfaces (Riekki et al., 2003).

In a multi-agent system, for example, a customer may want to place a long-distance call. O n c e this information is known, agents representing the carriers submit bids simultaneously. The bids are collected, and the best bid wins. In a complex system, the customer's agent may take the process one step f u r t h e r by showing all bidders the offers, allowing them to rebid or negotiate. This process is currently accomplished man- ually by increasing the number of companies that place projects and subassemblies up for bids in business-to-business electronic commerce (Turban et al., 2000).

F I G U R E 1 3 . 7 GENIE THE N E T : A SAMPLE MULTI-AGENT SYSTEM

C H A P T E R 13 INTELLIGENT SYSTEMS OVER THE INTERNET

A complex solution is decomposed into subproblems, each of which is assigned to an agent that works on the problem independently of others and is supported by a knowledge base. Information is acquired and interpreted by knowledge-processing

agents that use deductive and inductive methods as well as computations. The data are refined, interpreted, and sent to the coordinator, who transfers to the user interface whatever is relevant to the user's specific inquiry or need. Multimedia agents can orga- nize the presentation to fit individual executives. If no existing knowledge is available to answer an inquiry, knowledge creating and collecting agents of various types are triggered.