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In recent years, there has been a growing amount of research on inductive learning. Out of this research a number of promising algorithms have surfaced. In the paper after a brief description of knowledge acquisition, induction and inductive learning; RULES family of inductive learning algorithms, their strengths as well as weaknesses are explained and discussed. The applications of inductive learning and particularly the applications of RULES family of algorithms are overviewed.

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Wireless sensor and actor networks (WSAN) are captivating significant attention because of their suitability for safety-critical applications. Efficient actor placement in such applications is extremely desirable to perform effective and timely action across the deployment region. Nonetheless, harsh application environment inherently favors random placement of actors that leads to high concentration deployment and strangles coverage. Moreover, most of the published schemes lack rigorous validation and entirely rely on informal techniques (e.g., simulation) for evaluating nonfunctional properties of algorithms. This paper presents a localized movement control actor relocation (MCAR) algorithm that strives to improve connected coverage while minimizing movement overhead. MCAR pursues post-deployment actor repositioning in such a way that actors repel each other for better coverage while staying connected. We employ complementary formal and informal techniques for MCAR verification and validation. We model WSAN as a dynamic graph and transform MCAR to corresponding formal specification using Z notation. The resulting specification is analyzed and validated using Z eves tool. We simulate the specification to quantitatively demonstrate the efficiency of MCAR. Simulation results confirm the efficiency of MCAR in terms of movement overhead and connected coverage compared to contemporary schemes. The results show that MCAR can reduce distance movement up to 32 % while improving coverage up to 29 % compared to published schemes.

Inductive learning enables the system to recognize patterns and regularities in previous knowledge or training data and extract the general rules from them. In literature there are proposed two main categories of inductive learning methods and techniques. Divide-and-Conquer algorithms also called decision Tree algorithms and Separate-and-Conquer algorithms known as covering algorithms. This paper first briefly describe the concept of decision trees followed by a review of the well known existing decision tree algorithms including description of ID3, C4.5 and CART algorithms. A well known example of covering algorithms is RULe Extraction System (RULES) family. An up to date overview of RULES algorithms, and Rule Extractor-1 algorithm, their solidity as well as shortage are explained and discussed. Finally few application domains of inductive learning are presented.

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