2018
DOI: 10.1080/07474946.2018.1427971
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On the analysis of a random walk-jump chain with tree-based transitions and its applications to faulty dichotomous search

Abstract: Random Walks (RWs) have been extensively studied for more than a century [1]. These walks have traditionally been on a line, and the generalizations for two and three dimensions, have been by extending the random steps to the corresponding neighboring positions in one or many of the dimensions. Among the most popular RWs on a line are the various models for birth and death processes, renewal processes and the gambler's ruin problem. All of these RWs operate "on a discretized line", and the walk is achieved by … Show more

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Cited by 2 publications
(2 citation statements)
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“…The simple Tsetlin Automaton approach has formed the core for more advanced finite state learning automata designs that solve a wide range of problems. This includes resource allocation [7], decentralized control [8], knapsack problems [9], searching on the line [10,11], meta-learning [12], the satisfiability problem [13,14], graph colouring [14], preference learning [15], frequent itemset mining [16], adaptive sampling [17], spatio-temporal event detection [18], equi-partitioning [19], streaming sampling for social activity networks [20], routing bandwidthguaranteed paths [21], faulty dichotomous search [22], learning in deceptive environments [23], as well as routing in telecommunication networks [24]. The unique strength of all of these finite state learning automata solutions is that they provide state-of-the-art performance when problem properties are unknown and stochastic, while the problem must be solved as quickly as possible through trial and error.…”
Section: State-of-the-art In the Field Of Finite State Learning Automatamentioning
confidence: 99%
“…The simple Tsetlin Automaton approach has formed the core for more advanced finite state learning automata designs that solve a wide range of problems. This includes resource allocation [7], decentralized control [8], knapsack problems [9], searching on the line [10,11], meta-learning [12], the satisfiability problem [13,14], graph colouring [14], preference learning [15], frequent itemset mining [16], adaptive sampling [17], spatio-temporal event detection [18], equi-partitioning [19], streaming sampling for social activity networks [20], routing bandwidthguaranteed paths [21], faulty dichotomous search [22], learning in deceptive environments [23], as well as routing in telecommunication networks [24]. The unique strength of all of these finite state learning automata solutions is that they provide state-of-the-art performance when problem properties are unknown and stochastic, while the problem must be solved as quickly as possible through trial and error.…”
Section: State-of-the-art In the Field Of Finite State Learning Automatamentioning
confidence: 99%
“…The simple Tsetlin Automaton approach has formed the core of more advanced FSLA designs that solve a wide range of problems. This includes decentralized control [24], equi-partitioning [18], streaming sampling for social activity networks [7], faulty dichotomous search [28], and learning in deceptive environments [30], to list a few examples. The unique strength of all of these FSLA designs is that they provide state-of-the-art performance when problem properties are unknown and stochastic, and the problem must be solved as quickly as possible through trial and error.…”
Section: Introductionmentioning
confidence: 99%