Knowledge management is very important in the consulting business. On the one hand knowledge is the crucial factor of production that differentiates consulting companies from their competitors. On the other hand knowledge management is a consulting service that can be sold to other companies. The changes that have to be established during the introduction of knowledge management in mid-size consulting companies are presented. They comprise changes in organization, roles, processes and information technology. The main focus is set on information technology introducing an approach to support the process of capturing and transferring implicit knowledge with intelligent software agents.
We propose a theoretical reference framework for a Knowledge Management (KM) Information Technology (IT) system in organizations. We take a holistic perspective on KM and derive the required features for the framework from KMrelated research in psychology, business management and computer science. We propose classifications of organizational knowledge and knowledge entities (sets of people) either within an organization or associated with it. The framework comprises the derived features for the derived knowledge classes and knowledgentities. We motivate why agent technology is suitable for implementation of the framework.
In this paper we underline the importance of knowledge in artificial moral agents and describe our experience-focused approach which could help existing algorithms go beyond proofs of concept level and be tested for generality and real world usability. We point out the difficulties with implementation of current methods and their lack of contextual knowledge hindering simulations in more realistic, every-day life situations. The idea is to prioritize resources for predictions and the process of automatic knowledge acquisition for an oracle to be used by moral agents, both human and artificial.
Only knowing captures the intuitive notion that the beliefs of an agent are precisely those that follow from its knowledge base. While only knowing has a simple possible-world semantics in a single agent setting, the many agent case has turned out to be much more challenging. In a recent paper, we proposed an account which arguably extends only knowing to multiple agents in a natural way. However, the approach was limited in that the semantics cannot deal with infinitary notions such as common knowledge. In this work, we lift that serious limitation to obtain a first-order language with only knowing and common knowledge, allowing us to study the interaction between these notions for the very first time. By adding a simple form of public announcement, we then demonstrate how the muddy children puzzle can be cast in terms of logical implications given what is only known initially.
Agents in real-world environments may have only partial access to available information, often in an arbitrary, or hard to model, manner. By reasoning with knowledge at their disposal, agents may hope to recover some missing information. By acquiring the knowledge through a process of learning, the agents may further hope to guarantee that the recovered information is indeed correct. Assuming only a black-box access to a learning process and a prediction process that are able to cope with missing information in some principled manner, we examine how the two processes should interact so that they improve their overall joint performance. We identify natural scenarios under which the interleaving of the processes is provably beneficial over their independent use.