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1. *Knowledge Elicitation, Acquisition and Validation*: This is the process whereby KR are elicited and captured based on the role of individual actors. Thus, we use a case-based algorithm as knowledge acquisition method. In order to capture the tacit knowledge of actors, the communication
(socialization process as in table 1) among them is continuously tracked. The externalization of such knowledge requires the process of declaration and annotation. By declaration process, it implies actors have to declare the knowledge with respect to their roles and context at hand. The annotation process captures the recurrent communication among actors for the purpose of proper understanding and validation of KR.

The acquired KR undergoes validation to ensure high degree of certainty or reliability of such KR as conceded by actors. Thus, this phase basically requires a recursive process of declaration and annotation to capture KR in such a way that those previously captured are not replaced by new entry. Each entry of KR can be distinguished by the respective timestamp. 

Figure 3 illustrates this phase further using "TD" and "TA" to denote timestamp of KR declaration and annotation respectively.

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2. *Knowledge Representation: KR* is represented with the aid of a conceptual knowledge model. This model structures KR and its properties or description and existing relationship. 

The generic conceptual model depicted in figure 3 below represents the KR required in problem solving process in a given domain. Each component of the model can further be decomposed into more KR with regards to the given domain.

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3. *Storage with Temporal Attributes*: The acquired KR are stored with temporal attributes that is, date/time stamp in a CM 
which is called Knowledge Repository in the context of this work. KR in current projects are dynamically stored at real time as actors collaborate. The Knowledge Repository is structured such that date/time stamps are stored with each instance of KR.

4. *Knowledge Exploitation*: The storage of KR for instantiation of a Knowledge Repository facilitates knowledge reuse and sharing through exploitation process. We adopt EQUA2te (Explore, Query, Analyze and Annotate) model [27]
for knowledge exploitation. EQUA2te tailors the query terms by proposing attributes of the content of Knowledge Repository to aid retrieval of relevant knowledge that meets actors" needs. The search algorithm adopts case-based reasoning approach for retrieval of different cases of knowledge resources such as, participatory actors, process of resolution of decision problem and the result of various activities.

5. *Feedback Exploitation Strategy*: This process allows capturing actor"s assessment of relevance of exploited knowledge for new problem cases. This strategy enables the evaluation and evolution of the system in such a way that actors can further exploit the generated feedback. This implies that exploitation can lead to feedback generation which subsequently can be exploited.

Thus, the system becomes dynamic as a result of continuous update from knowledge reuse. This KM approach is applied to 