This paper presents a new formal approach to the modelling of information reuse and integration for innovation. Not all information is useful for innovation, and many ideas do not become profitable. We believe that information resources should not only be available, but also should be capable and compatible with the required information/needs. Use of relevant tools for information management should improve the capacity for effective decision making for innovation. Use of data mining technologies for the extraction of potentially useful information may not always produce the required information. Hidden or previously unknown information may be found in datasets, but the required information for innovation may not be in the datasets. There is a need for the development of techniques to ensure that decision makers are provided with capable and compatible information. Profile Theory is used for the analysis and modelling of reuse and integration of available information.
This chapter presents a project proposal, which defines future work in engineering the learning systems. This proposal outlines a number of directions in the fields of systems engineering, machine learning, knowledge engineering, and profile theory, that lead to the development of formal methods for the modeling and engineering of learning systems. This chapter describes a framework for formalisation and engineering the cognitive processes, which is based on applications of computational methods. The proposed work studies cognitive processes, and considers a cognitive system as a multi-agents system of human-cognitive agents. It is important to note that this framework can be applied to different types of learning systems, and there are various techniques from different theories (e.g., system theory, quantum theory, neural networks) can be used for the description of cognitive systems, which in turn can be represented by different types of cognitive agents.
Traditionally multi-agent learning is considered as the intersection of two subfields of artificial intelligence: multi-agent systems and machine learning. Conventional machine learning involves a single agent that is trying to maximise some utility function without any awareness of existence of other agents in the environment (Mitchell, 1997). Meanwhile, multi-agent systems consider mechanisms for the interaction of autonomous agents. Learning system is defined as a system where an agent learns to interact with other agents (e.g., Clouse, 1996; Crites & Barto, 1998; Parsons, Wooldridge & Amgoud, 2003). There are two problems that agents need to overcome in order to interact with each other to reach their individual or shared goals: since agents can be available/unavailable (i.e., they might appear and/or disappear at any time), they must be able to find each other, and they must be able to interact (Jennings, Sycara & Wooldridge, 1998).
Information technology is used to help learners gain the knowledge, confidence, and credentials they need to succeed. Contemporary approaches to online learning offer technology-mediated courses. The educational programs offered take full advantage of the power of the Internet to foster communication, learning, and skill and knowledge acquisition.
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