Search results for "Learning automata"
showing 6 items of 76 documents
Combining finite learning automata with GSAT for the satisfiability problem
2010
A large number of problems that occur in knowledge-representation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. The growing need for more efficient and scalable algorithms has led to the development of a large number of SAT solvers. This paper reports the first approach that combines finite learning automata with the greedy satisfiability algorithm (GSAT). In brief, we introduce a new algorithm that integrates finite learning automata and traditional GSAT use…
Optimizing channel selection for cognitive radio networks using a distributed Bayesian learning automata-based approach
2015
Consider a multi-channel Cognitive Radio Network (CRN) with multiple Primary Users (PUs), and multiple Secondary Users (SUs) competing for access to the channels. In this scenario, it is essential for SUs to avoid collision among one another while maintaining efficient usage of the available transmission opportunities. We investigate two channel access schemes. In the first model, an SU selects a channel and sends a packet directly without Carrier Sensing (CS) whenever the PU is absent on this channel. In the second model, an SU invokes CS in order to avoid collision among co-channel SUs. For each model, we analyze the channel selection problem and prove that it is a so-called "Exact Potent…
Solving Graph Coloring Problems Using Learning Automata
2008
The graph coloring problem (GCP) is a widely studied combinatorial optimization problem with numerous applications, including time tabling, frequency assignment, and register allocation. The growing need for more efficient algorithms has led to the development of several GCP solvers. In this paper, we introduce the first GCP solver that is based on Learning Automata (LA). We enhance traditional Random Walk with LA-based learning capability, encoding the GCP as a Boolean satisfiability problem (SAT). Extensive experiments demonstrate that the LA significantly improve the performance of RW, thus laying the foundation for novel LA-based solutions to the GCP.
Learning Automaton Based On-Line Discovery and Tracking of Spatio-temporal Event Patterns
2010
Published version of an article from the book: Lecture Notes in Computer Science, 2010, Volume 6230/2010, 327-338. The original publication is available at Springerlink. http://dx.doi.org/10.1007/978-3-642-15246-7_31 Discovering and tracking of spatio-temporal patterns in noisy sequences of events is a difficult task that has become increasingly pertinent due to recent advances in ubiquitous computing, such as community-based social networking applications. The core activities for applications of this class include the sharing and notification of events, and the importance and usefulness of these functionalites increases as event-sharing expands into larger areas of one’s life. Ironically, …
On solving single elevator-like problems using a learning automata-based paradigm
2020
This paper concentrates on a host of problems with characteristics similar to those that are related to moving elevators within a building. These are referred to as Elevator-like problems (ELPs), and their common phenomena will be expanded on in the body of the paper. We shall resolve ELPs using a subfield of AI, namely the field of learning automata (LA). Rather than working with the well-established mathematical formulations of the field, our intention is to use these tools to tackle ELPs, and in particular, those that deal with single “elevators” moving between “floors”. ELPs have not been tackled before using AI. In a simplified domain, the ELP involves the problem of optimizing the sch…
The Bayesian Pursuit Algorithm: A New Family of Estimator Learning Automata
2011
Published version of a chapter in the book: Modern Approaches in Applied Intelligence. Also available from the publisher at http://dx.doi.org/10.1007/978-3-642-21827-9_53 The fastest Learning Automata (LA) algorithms currently available come from the family of estimator algorithms. The Pursuit algorithm (PST), a pioneering scheme in the estimator family, obtains its superior learning speed by using Maximum Likelihood (ML) estimates to pursue the action currently perceived as being optimal. Recently, a Bayesian LA (BLA) was introduced, and empirical results that demonstrated its advantages over established top performers, including the PST scheme, were reported. The BLA scheme is inherently …