Efficient planning plays a crucial role in model-based reinforcement learning. Traditionally, the main planning operation is a full backup based on the current estimates of the successor states. Consequently, its computation time is proportional to the number of successor states. In this paper, we introduce a new planning backup that uses only the current value of a single successor state and h...

Given a bounded universe {0, 1, . . . , U−1}, we show how to perform (successor) searches in O(log log ∆) expected time and updates in O(log log ∆) expected amortized time, where ∆ is the rank difference between the element being searched for and its successor in the structure. This unifies the results of traditional bounded universe structures (which support successor searches in O(log logU) t...

This article explores whether new firms managed by founder-chief executive officers (CEOs) are more likely to survive than those successor-CEOs in times of crisis. Drawing on the concept ‘resilience’ adversity, we argue that founder-CEOs increase likelihood firm survival, especially Using a sample Japanese founded during 2003–2010 period, examine impact founder-CEO succession survival. The anal...

Stemming algorithms find canonical forms for inflected words, e. g. for declined nouns or conjugated verbs. Since such a unification of words with respect to gender, number, time, and case is a language-specific issue, stemming algorithms operationalize a set of linguistically motivated rules for the language in question. The most well-known rule-based algorithm for the English language is from...