Expert Information and Advising system Research proposal

Ali Ghalehban Zanjanab 2013 Knowledge Management

Expert Information & Advising system

Ali Ghalehban Zanjanab

Research proposal for the degree of

Doctor of Philosophy (PhD) in Information Technology

Knowledge Management Course

at Assumption university of Thailand

2013

1. RESEARCH TOPIC

Expert systems, artificial intelligence and other informatics systems which now days

are using for advising always have limitations on the decision making and giving

strict advise, The main cause of this issue could return to the principles and methods

that this systems use to operate. In computer operation with binary values, Boolean

logic can be used to describe electromagnetically charged memory locations or

circuit states that are either charged (1 or true) or not charged (0 or false). The

computer can use an AND gate or an OR gate operation to obtain a result that can

be used for further processing. this is a simple machine decision making but this

method have limit and if use it to compare or making decision between issues we

won’t get good result in this research, researcher is going to explain what other

methods can be used to process information and get best advising from machine .in

this case Researcher decide to solve this problem and develop a system which can

give best advising based on organization information base.

2. BACKGROUND

Many organizations today are faced with a multitude of information and knowledge,

but while there is often a large amount of information and knowledge in

organization, given that many of these organizations do not have proper information

system they are Unable to obtain the desired result from information .Then

researcher decide to develop a system to give most strict advise based on each

organization information base.

3. RESEARCH AREA

This research will focus expert advising system which will work as an expert

information and advising system in each organization to give most strict advises to

improve and develop organization in different fields.

4. RATIONALE

Currently there are some similar systems which are working in this area to help

organizations and advise them based on each organization information base, but this

system will need to part to get final result , first part is about gathering data,

Information & knowledge and process them knowledge until system can make

decision based on this processed information .

Currently Data, information, Knowledge gathering and processing methods

Collective Intelligence: is shared or group intelligence that emerges from the

collaboration, collective efforts, and competition of many individuals and appears in

consensus decision making. The term appears in sociobiology, political science and

in context of mass peer review and crowdsourcing applications. It may involve

consensus, social capital and formalisms such as voting systems, social media and

other means of quantifying mass activity. Collective IQ is a measure of collective

intelligence, although it is often used interchangeably with the term collective

intelligence.

- Civic Intelligence: is an "intelligence" that is devoted to addressing public or civic

issues. The term has been applied to individuals and, more commonly, to collective

bodies, like organizations, institutions, or societies.

- Collaborative intelligence: is a term used in several disciplines, and has several

different meanings. In a business setting, it can describe the result of accessing a

network of people. It is also used to denote non-anonymous heterogeneity in multi-

agent problem-solving systems.

- Collective memory: refers to the shared pool of information held in the memories

of two or more members of a group.

- Crowd sourcing: is the practice of obtaining needed services, ideas, or content by

soliciting contributions from a large group of people, and especially from an online

community, rather than from traditional employees or suppliers.

- Swarm intelligence: is the collective behavior of decentralized, self-organized

systems, natural or artificial.

- Group decision making: is a situation faced when individuals collectively make

a choice from the alternatives before them. This decision is no longer attributable to

any single individual who is a member of the group. This is because all the

individuals and social group processes such as social influence contribute to the

outcome. The decisions made by groups are often different from those made by

individuals

- Judge adviser system: is a type of advice structure often studied in advice taking

research, a subset of decision-making in the social sciences. The two roles in a JAS

are the judge and advisor roles. The judge is the decision maker who evaluates

information concerning a particular decision and makes the final judgment on the

decision outcome.

- Connectivity (Graph Theory): is one of the basic concepts of graph theory: it

asks for the minimum number of elements (nodes or edges) which need to be

removed to disconnect the remaining nodes from each other .It is closely related to

the theory of network flow problems. The connectivity of a graph is an important

measure of its robustness as a network.

- Recommender system: is a subclass of information filtering system that seek to

predict the 'rating' or 'preference' that user would give to an item. Recommender

systems have become extremely common in recent years, and are applied in a variety

of applications

- Group polarization: refers to the tendency for groups to make decisions that are

more extreme than the initial inclination of its members. These more extreme

decisions are towards greater risk if individuals' initial tendencies are to be risky and

towards greater caution if individuals' initial tendencies are to be cautious.

5. PROGRESS

Above mentioned methods are current ways which organizations used to gathering

knowledge and advising system, but as researcher mentioned Many organizations

today are faced with a multitude of information and knowledge, but while there is

often a large amount of information and knowledge in organization, given that many

of these organizations do not have proper information system they are Unable to

obtain the desired result from information .Then researcher decide to develop a

system to give most strict advise based on each organization information base , then

for solve this limit and get best result and strict advise from system below methods

are proposed to use to get best result .

- Fuzzy Logic: is a form of many-valued logic; it deals with reasoning that is

approximate rather than fixed and exact. Compared to traditional binary sets (where

variables may take on true or false values) fuzzy logic variables may have a truth

value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle

the concept of partial truth, where the truth value may range between completely

true and completely false.

- Adaptive neuron fuzzy inference system (ANFIS): is a kind of neural network

that is based on Takagi– Sugeno fuzzy inference system. Since it integrates both

neural networks and fuzzy logic principles, it has potential to capture the benefits of

both in a single framework. Its inference system corresponds to a set of fuzzy IF–

THEN rules that have learning capability to approximate nonlinear functions. Hence,

ANFIS is considered to be a universal estimator.

- Artificial neural networks: Are computational models inspired by animal central

nervous systems (in particular the brain) that are capable of machine learning and

pattern recognition. They are usually presented as systems of interconnected

"neurons" that can compute values from inputs by feeding information through the

network.

- Expert system: is a computer system that emulates the decision-making ability of

a human expert .expert systems are designed to solve complex problems by

reasoning about knowledge, represented primarily as IF-THEN rules rather than

through conventional procedural code.