Learning in abstract memory schemes for dynamic optimization

We investigate an abstraction based memory scheme for evolutionary algorithms in dynamic environments. In this scheme, the abstraction of good solutions (i.e., their approximate location in the search space) is stored in the memory instead of good solutions themselves and is employed to improve future problem solving. In particular, this paper shows how learning takes place in the abstract memory scheme and how the performance in problem solving changes over time for different kinds of dynamics in the fitness landscape. The experiments show that the abstract memory enables learning processes and efficiently improves the performance of evolutionary algorithms in dynamic environments

An Integrated Planning Representation Using Macros, Abstractions, and Cases

Planning will be an essential part of future autonomous robots and integrated intelligent systems. This paper focuses on learning problem solving knowledge in planning systems. The system is based on a common representation for macros, abstractions, and cases. Therefore, it is able to exploit both classical and case based techniques. The general operators in a successful plan derivation would be assessed for their potential usefulness, and some stored. The feasibility of this approach was studied through the implementation of a learning system for abstraction. New macros are motivated by trying to improve the operatorset. One heuristic used to improve the operator set is generating operators with more general preconditions than existing ones. This heuristic leads naturally to abstraction hierarchies. This investigation showed promising results on the towers of Hanoi problem. The paper concludes by describing methods for learning other problem solving knowledge. This knowledge can be represented by allowing operators at different levels of abstraction in a refinement

Problem-Solving and Computational Thinking Practices: Lesson Learned from The Implementation of ExPRession Model

Computational thinking ability is one of today's problem-solving methods that can be applied in physics learning. However, it is not yet known by most teachers so it has not been applied optimally in learning activities. This study aims to identify students' problem-solving and computational thinking abilities in solving well-structure physics problems. The subject of this study was the eleven grade majoring in natural science of SMAN 1 Bangunrejo. This type of research is descriptive research. The data used to analyze the students' problem-solving and computational thinking abilities were obtained from the essay test. Based on the results of descriptive analysis, it can be concluded that there is a relationship between students' problem-solving abilities and students' computational thinking abilities. In making a useful description, abstraction and decomposition abilities are needed, while to determine the physics approach and specific application of physics, generalization abiliy are needed. In solving mathematical procedures, algorithm ability are needed and to find out logical progressions, debugging ability are needed

Problem-Solving and Computational Thinking Practices: Lesson Learned from The Implementation of ExPRession Model

Computational thinking ability is one of today's problem-solving methods that can be applied in physics learning. However, it is not yet known by most teachers so it has not been applied optimally in learning activities. This study aims to identify students' problem-solving and computational thinking abilities in solving well-structure physics problems. The subject of this study was the eleven grade majoring in natural science of SMAN 1 Bangunrejo. This type of research is descriptive research. The data used to analyze the students' problem-solving and computational thinking abilities were obtained from the essay test. Based on the results of descriptive analysis, it can be concluded that there is a relationship between students' problem-solving abilities and students' computational thinking abilities. In making a useful description, abstraction and decomposition abilities are needed, while to determine the physics approach and specific application of physics, generalization abiliy are needed. In solving mathematical procedures, algorithm ability are needed and to find out logical progressions, debugging ability are needed

A hierarchical reinforcement learning method for persistent time-sensitive tasks

Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight. However, little effort has been put into developing methods to learn policies for complex persistent tasks and tasks that are time-sensitive. In this paper, we take a step towards solving this problem by using signal temporal logic (STL) as task specification, and taking advantage of the temporal abstraction feature that the options framework provide. We show via simulation that a relatively easy to implement algorithm that combines STL and options can learn a satisfactory policy with a small number of training cases

A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks

Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight. However, little effort has been put into developing methods to learn policies for complex persistent tasks and tasks that are time-sensitive. In this paper, we take a step towards solving this problem by using signal temporal logic (STL) as task specification, and taking advantage of the temporal abstraction feature that the options framework provide. We show via simulation that a relatively easy to implement algorithm that combines STL and options can learn a satisfactory policy with a small number of training case

Student activities in solving mathematics problems with a computational thinking using Scratch

The progress of the times requires students to be able to think quickly. Student activities in learning are always associated with technology and students’ thinking activities and are expected to think computationally. Therefore, this study aimed to determine how learning with the concept of computational thinking (CT) using the Scratch program can improve students’ mathematical problem-solving abilities. An exploratory research design was conducted by involving 132 grade VIII students in Kuningan, Indonesia. Data analysis began with organization, data description, and statistical testing. The results showed that students performed the concepts of abstraction thinking, algorithmic thinking, decomposition, and evaluation in solving mathematical problems. There were differences in students’ problem-solving abilities before and after the intervention. Students’ activeness in solving problems using the CT concept through a calculator significantly affected 52.3% of the ability to solve mathematical problems

A matter of time: Implicit acquisition of recursive sequence structures

A dominant hypothesis in empirical research on the evolution of language is the following: the fundamental difference between animal and human communication systems is captured by the distinction between regular and more complex non-regular grammars. Studies reporting successful artificial grammar learning of nested recursive structures and imaging studies of the same have methodological shortcomings since they typically allow explicit problem solving strategies and this has been shown to account for the learning effect in subsequent behavioral studies. The present study overcomes these shortcomings by using subtle violations of agreement structure in a preference classification task. In contrast to the studies conducted so far, we use an implicit learning paradigm, allowing the time needed for both abstraction processes and consolidation to take place. Our results demonstrate robust implicit learning of recursively embedded structures (context-free grammar) and recursive structures with cross-dependencies (context-sensitive grammar) in an artificial grammar learning task spanning 9 days. Keywords: Implicit artificial grammar learning; centre embedded; cross-dependency; implicit learning; context-sensitive grammar; context-free grammar; regular grammar; non-regular gramma

PROFILE OF STUDENTS’ COMPUTATIONAL THINKING BASED ON SELF-REGULATED LEARNING IN COMPLETING BEBRAS TASKS

Bebras task is a problem solving problem that integrates computational thinking in it, which the stages in computational thinking consist of: decomposition, abstraction, algorithm, and pattern recognition. This study aims to describe the profile of student’s computational thinking based on the level of self-regulated learning in completing bebras task. This study is a qualitative-descriptive study with three research subjects based on the level of students’ self-regulated learning, namely high self-regulated learning, medium self-regulated learning, and low self-regulated learning. The results of this study indicate that students with different levels of self-regulated learning have different computational thinking ability in completing bebras task. Student with high level of self-regulated learning can reach the stages of decomposition, abstraction, algorithm, and pattern recognition. Student with medium level of self-regulated learning can reach the stages of decomposition, absraction, and algorithm. Student with low level of self-regulated learning can reach the stage of decomposition only. Student with low level of self-regulated learning do not yet reflect independence in learning