This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data.Īn extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. These techniques are assessed, and used to gain insights into both nonlinear dynamical systems and strategic decision making in adversarial environments.ĭata mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining big data mining big data mining and Internet of Things (IoT) and educational data mining. The performance of this new solution technique is compared to traditional exact solvers, which traverse the entire game tree, as well as to approximate solvers such as Myopic and Monte Carlo Tree Search (MCTS). To resolve this, we introduce a novel Nash Dominant solver, which is both numerically efficient and exact. Due to their size, the computational cost of solving these BKL games becomes the dominant factor in the solution process. This paper studies a class of decision problems on the BKL model, where a large set of coupled, switching dynamical systems are analysed using game-theoretic methods. Modelling these interactions with the so-called Boyd-Kuramoto-Lanchester (BKL) complex dynamical system model yields results that can be applied to business, gaming and security contexts.

Here the competitors try to both positively influence their own returns, while negatively affecting those of their competitors.

We apply computational Game Theory to a unification of physics-based models that represent decision-making across a number of agents within both cooperative and competitive processes.

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