D-rules: learning & planning

One current research goal of Artificial Intelligence and Machine Learning is to improve the problem-solving performance of systems with their own experience or from external teaching. The work presented in this paper concentrates on the learning of decomposition rules, also called d-rules, i.e., given some examples learn rules that guide the planning process, in new problems, by determining what operators are to be included in the solution plan. Also a planning algorithm is presented that uses the learned d-rules in order to obtain the desired plan. The learning algorithm includes a value function approximation, which gives each learned rule an associated function. If the planner finds more than one applicable d-rule, it discriminates among them using this feature. Decomposition rules have been learned in the blocks world domain, and those d-rules have been used by the planner to solve new problems.VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

D-rules: learning & planning

One current research goal of Artificial Intelligence and Machine Learning is to improve the problem-solving performance of systems with their own experience or from external teaching. The work presented in this paper concentrates on the learning of decomposition rules, also called d-rules, i.e., given some examples learn rules that guide the planning process, in new problems, by determining what operators are to be included in the solution plan. Also a planning algorithm is presented that uses the learned d-rules in order to obtain the desired plan. The learning algorithm includes a value function approximation, which gives each learned rule an associated function. If the planner finds more than one applicable d-rule, it discriminates among them using this feature. Decomposition rules have been learned in the blocks world domain, and those d-rules have been used by the planner to solve new problems.VI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Empirical speedup learning of decomposition rules for planning

One current research goal of Artificial Intelligence and Machine Learning is to build learning systems that robustly improve their planning performance with experience [Tade91]. This work concentrates on learning decomposition rules, i.e., learning rules that guide the planning process by determining the order in which operators are to be applied and how they are to be bound in specific states. A domain-independent learning algorithm that is capable of learning such rules from teacher-given examples has been designed and implemented. Decomposition rules have been learned in the blocks world domain , and it is shown that a small number of examples are sufficient to achieve very high success rates.Keywords: Empirical learning , speedup learning, decomposition rules, maximally specific generalizations , subset queries

Preface to the Best Papers from CIESC 2013 Special Issue

Since 1999, the Iberoamerican Congress on Higher Education in Computing (CIESC) has been held as one of the main associated scientific events of the Latin American Conference on Informatics (CLEI). Its purpose has been to stimulate and provide a space for academics and professionals to discuss topics centered on undergraduate and graduate education in Computing

Learning and Motivation When Using Multiple-Try in a Digital Game for Primary Students in Chile

The number of attempts to provide students is a key instructional characteristic in computer-based learning (CBL). However, it has not been covered extensively, and there is a need to delve deeper into the factors affecting multiple-try performance and allowing its successful use, including the learner’s involved emotional processes. This study examines the effects of multiple-try on a drill-and-practice mathematical game devoted to primary school students. A total of 73 students from four courses from two schools participated in the experiment. They were randomly assigned to a 3-attempt multiple-try (MTF) and a single-try knowledge of correct response (KCR) conditions. The study covered impacts on learning performance, together with motivation, effort, pressure, and the value of students regarding the learning activity based on the self-determination theory (SDT) perspective and its cognitive evaluation sub-theory (CET). The study’s main findings were that (a) the MTF condition outperformed KCR in terms of students’ learning gains; (b) MTF presented higher levels of perceived competence and autonomy, which, according to SDT, fosters motivation and learning; (c) a cost was yielded in students’ perceived pressure under MTF; and (d) perceived effort and value was similarly high for both conditions despite learning differences. This study complements the existing literature on multiple-try, providing insights into what conditions are beneficial for multiple-try use