Some domains, such as real‐time strategy (RTS) games, pose several challenges to traditional planning and machine learning techniques. In this article, we present a novel on‐line case‐based planning architecture that addresses some of these problems. Our architecture addresses issues of plan acquisition, on‐line plan execution, interleaved planning and execution, and on‐line plan adaptation. We also introduce the Darmok system, which implements this architecture to play Wargus (an open source clone of the well‐known RTS game Warcraft II). We present empirical evaluation of the performance of Darmok and show that it successfully learns to play the Wargus game.

Associative classification, which originates from numerical data mining, has been applied to deal with text data recently. Text data is firstly digitalized to database of transactions, and then training and prediction is actually conducted on the derived numerical dataset. This intuitive strategy has demonstrated quite good performance. However, it doesn't take into consideration the inherent characteristics of text data as much as possible, although it has to deal with some specific problems of text data such as lemmatizing and stemming during digitalization. In this paper, we propose a bottom-up strategy to adapt associative classification to text categorization, in which we take into account structure information of text. Experiments on Reuters-21578 dataset show that the proposed strategy can make use of text structure information and achieve better performance.