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In this paper, a high-efficiency knowledge management system, HDIA_KMS, based on habitual domains and intelligent agents is proposed. The design concept of the system is to use an agent community to deal with the activities of knowledge management effectively, that is, knowledge sharing, knowledge classification, knowledge creation, and knowledge recovery. The features of this system are: collecting the surfing habits of each user and finding hidden patterns of departments to recommend articles by data mining; classifying the knowledge articles in accordance with departments and therefore rapidly finding out suitable documents for users; providing personal service through personal profiles; creating new knowledge documents by discussing among users; accepting and reclassifying improperly classified articles found by users; taking the abilities of collaboration, independence and automation of agents to help users use and improve the effects of HDIA_KMS. The PASSI methodology is adopted to analyze and design the system, and agents protocols follow the FIPA specifications.

In this paper, we discuss how information technology (IT) affects and influences people to make decisions. We first introduce human behavior mechanism and habitual domains — the software that drive the behaviors. Then we discuss the impacts of IT on decision elements and environment, and then IT's impacts on a variety of decision problems including routine problems, mixed routine problems, fuzzy problems and challenging problems. IT is useful in solving routine problems but not as obvious in solving fuzzy and challenging problems. To solve fuzzy and challenging problems, an effective concept and model of competence set analysis is introduced. Finally, we describe three types of competence set analysis and show how IT can help in these three types of problems.

In this article, we show that, in high-tech industries, there are significant differences in the Habitual Domains (HD) of technologists/researchers (T/R) and managers/leaders (M/L). The differences are measured specifically in the following: characteristics, attitudes toward career and life, perception of business problems, business competences and resources. We then describe how a T/R can effectively transform himself/herself into a successful M/L, by transforming his/her HD closer to that of a successful M/L.

There are many parameters in challenging decision problems, including the alternatives, the criteria, resources, the perception of decision problems, decision makers and their psychological states, information inputs from the environment, and self-suggestion, etc. At any moment of time, some of these parameters can catch our attention, called alerted parameters; some cannot, called unalerted parameters. Some parameters are visible, some are invisible. In addition, the parameters themselves can vary over certain ranges or domains. All of these make challenging decision problems very complex. We call this kind of problems as decision problems with changeable spaces (parameters).

We may focus on certain parameters with certain assumed values to find an "optimal" solution, which may lead to solve wrong problem with bad solution. Quite often, our focus may be just a small part of what we know, or just a part of what we are most familiar with. We may often neglect what we are not familiar with, and pay no attention to what we do not know. As a consequence, we may see just a small part of the problem domain (including all parameters and their possible variations over time). The portion (of the problem domain) that we cannot see is our decision blind. Suppose our alerted domain (those parameters and their variations that are currently under our consideration) to be fixed in only a small part of the problem domains. Then very likely we could end up with a serious mistake. This situation is known as decision trap.

In this article, we will introduce a systematic scheme, based on habitual domain theory, to help us reduce decision blinds and avoid decision traps so that we could make decision with good quality. Then we will also introduce the concept of competence set analysis to help us cope with challenging decision problems. This including: (i) how to effectively expand our competence (resources, skill, know-how, information, ideas, effort, etc.) as to solve a given problem effectively; and (ii) given a set of competence, how to maximize its value by solving a set of value added problems. Furthermore, we will introduce innovation dynamics which describe the dynamics of how to solve a set of problems with our existent or acquired competence (to relieve the pains or frustrations of "certain customers or decision makers" at certain situations) as to create value, and how to distribute this created value so that we can continuously expand out competence set to solve more challenging problems and create more value.

Challenging decision problems in changeable spaces are characterized by existence of complex decision parameters that are changing with time and situations, including criteria and alternatives. Some of these parameters may be critical for their effective solutions, but hidden in the depth of potential domains. In this rapidly changing world, including technology and attitude, without paying attention to the problems in changeable spaces, we could easily commit serious mistakes due to decision blinds, decision traps and/or decision shocks. The article starts with a brief description of the evolution of MCDM toward challenging problems in changeable spaces. Then it briefly sketches a dynamic human behavior mechanism and habitual domain theory which provide an effective list for us to search relevant decision parameters and pave the way for latter discussion. Competence set analysis, derived from habitual domain, is then introduced to exemplify decision blinds, decision traps and decision shocks in challenging decision problems. Checking lists and methods for discovering blinds and traps and for dealing with shocks are also provided. Innovation dynamics, a systematic network of thoughts, is introduced to further look out relevant key parameters in dynamic challenging problems. The related academic subjects in each link of the innovation dynamics are also explained, which allow us to see the complexity and interconnectivities among different challenging problems in changeable spaces. Finally we introduce three habitual domain tool boxes to empower ourselves to expand and enrich our thoughts into the depth of the potential domains of the challenging problems, which allows us to more effectively identify hidden parameters, problems and competence sets to reduce decision blinds, avoid decision traps and solve the problems, or dissolve the problems before they occur.