The main idea of this thesis is to define and formulate the role of cognitive control in cognitive dynamic systems and complex networks in order to control the directed flow of information. A cognitive dynamic system is based on Fuster's principles of cognition, the most basic of which is the so-called global perception-action cycle, that the other three build on. Cognitive control, by definition, completes the executive part of this important cycle. In this thesis, we first provide the rationales for defining cognitive control in a way that it suits engineering requirements. To this end, the novel idea of entropic state and thereby the two-state model is first described. Next, on the sole basis of entropic state and the concept of directed information flow, we formulate the learning algorithm as the first process of cognitive control. Most importantly, we show that the derived algorithm is indeed a special case of the celebrated Bellman's dynamic programming. Another significant key point is that cognitive control intrinsically differs from the generic dynamic programming and its approximations (commonly known as reinforcement learning) in that it is stateless by definition. As a result, the main two desired characteristics of the derived algorithm are described as follows: a) it is convergent to optimal policy, and b) it is free of curse of dimensionality.
Next, the predictive planning is described as the second process of cognitive control. The planning process is on the basis of shunt cycles (called mutually composite cycles herein) to bypass the environment and facilitate the prediction of future global perception-action cycles. Our results demonstrate predictive planning to have a very significant improvement to the functionality of cognitive control. We also deploy the explore/exploit strategy in order to apply a simplistic form of executive attention.
The thesis is then expanded by applying cognitive control into two different applications of practical importance. The first one involves cognitive tracking radar, which is based on a benchmark example and provides the means for testing the theory. In order to have a frame of reference, the results are compared to other cognitive controllers, which use traditional Q-learning and the method of dynamic optimization. In both cases, the new algorithm demonstrates considerable improvement with less computational load.
For the second application, the problem of observability in stochastic complex networks has been picked due to its importance in many practical situations. Having known cognitive control theory and its significant performance, the idea here is to view the network as the environment of a cognitive dynamic system; thereby, cognitive dynamic system with the cognitive controller plays a supervisory role over the network. The proposed methodology differs from the state-of-the-art in the literature in two accounts: 1) stochasticity both in modelling as well as monitoring processes, and 2) complexity in terms of edge density. We present several examples to demonstrate the information processing power of cognitive control in this context too.
The thesis will finish by drawing line for future research in three main directions. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/16708 |
| Date | 29 January 2015 |
| Creators | FATEMI BOOSHEHRI, SEYED MEHDI |
| Contributors | HAYKIN, SIMON, Computational Engineering and Science |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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