Schema Independent Relational Learning

Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions

BEHAVIOR BASED CONTROL AND FUZZY Q-LEARNING FOR AUTONOMOUS FIVE LEGS ROBOT NAVIGATION

This paper presents collaboration of behavior based control and fuzzy Q-learning for five legs robot navigation systems. There are many fuzzy Q-learning algorithms that have been proposed to yield individual behavior like obstacle avoidance, find target and so on. However, for complicated tasks, it is needed to combine all behaviors in one control schema using behavior based control. Based this fact, this paper proposes a control schema that incorporate fuzzy q-learning in behavior based schema to overcome complicated tasks in navigation systems of autonomous five legs robot. In the proposed schema, there are two behaviors which is learned by fuzzy q-learning. Other behaviors is constructed in design step. All behaviors are coordinated by hierarchical hybrid coordination node. Simulation results demonstrate that the robot with proposed schema is able to learn the right policy, to avoid obstacle and to find the target. However, Fuzzy q-learning failed to give right policy for the robot to avoid collision in the corner location. Keywords : behavior based control, fuzzy q-learnin

Learning-based constraints on schemata

Schemata are frequently used in cognitive science as a descriptive framework for explaining the units of knowledge. However, the specific properties which comprise a schema are not consistent across authors. In this paper we attempt to ground the concept of a schema based on constraints arising from issues of learning. To do this, we consider the different forms of schemata used in computational models of learning. We propose a framework for comparing forms of schemata which is based on the underlying representation used by each model, and the mechanisms used for learning and retrieving information from its memory. Based on these three characteristics, we compare examples from three classes of model, identified by their underlying representations, specifically: neural network, production-rule and symbolic network models

SB-CoRLA: Schema-Based Constructivist Robot Learning Architecture

This dissertation explores schema-based robot learning. I developed SB-CoRLA (Schema- Based, Constructivist Robot Learning Architecture) to address the issue of constructivist robot learning in a schema-based robot system. The SB-CoRLA architecture extends the previously developed ASyMTRe (Automated Synthesis of Multi-team member Task solutions through software Reconfiguration) architecture to enable constructivist learning for multi-robot team tasks. The schema-based ASyMTRe architecture has successfully solved the problem of automatically synthesizing task solutions based on robot capabilities. However, it does not include a learning ability. Nothing is learned from past experience; therefore, each time a new task needs to be assigned to a new team of robots, the search process for a solution starts anew. Furthermore, it is not possible for the robot to develop a new behavior. The complete SB-CoRLA architecture includes off-line learning and online learning processes. For my dissertation, I implemented a schema chunking process within the framework of SB-CoRLA that involves off-line evolutionary learning of partial solutions (also called “chunks”), and online solution search using learned chunks. The chunks are higher level building blocks than the original schemas. They have similar interfaces to the original schemas, and can be used in an extended version of the ASyMTRe online solution searching process. SB-CoRLA can include other learning processes such as an online learning process that uses a combination of exploration and a goal-directed feedback evaluation process to develop new behaviors by modifying and extending existing schemas. The online learning process is planned for future work. The significance of this work is the development of an architecture that enables continuous, constructivist learning by incorporating learning capabilities in a schema-based robot system, thus allowing robot teams to re-use previous task solutions for both existing and new tasks, to build up more abstract schema chunks, as well as to develop new schemas. The schema chunking process can generate solutions in certain situations when the centralized ASyMTRe cannot find solutions in a timely manner. The chunks can be re-used for different applications, hence improving the search efficiency

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

AN EXPLORATION OF THE QUANTITATIVE SIMULATION OF SCHEMA FORMATION - A CASE OF EARLY CHILDHOOD CHINESE LITERACY LEARNING

A schema is an intrinsic cognitive structure that helps an individual to organize all the messages absorbed and form a knowledge structure, which functions in helping one to learn and comprehend new things. Most studies of schemas have focused on schema-based teaching and explored learning outcomes, but few studies have discussed the development and construction of such schemas. This study centers on the issue of young children learning Chinese characters, explores the formation process of a schema, and attempts to quantify the establishment of a schema in an innovative research design. This study adopts an experimental approach with the process of intervention teaching, and makes comparisons between the pre-test and post-test to measure learning effectiveness. The results of this study verify that the schema theory does not exist only as an abstract concept, but also as one which can be recorded and described quantitatively. The result also indicates the number of teaching times and the accumulation of learning experience that are required in the establishment of a schema for young children learning Chinese characters. This study proves again that the schema theory has a positive impact on learning effectiveness.  Article visualizations

Implementing Schema-Based Instruction in the Elementary Classroom (Project)

Solving mathematical word problems is an ongoing problem for students with both reading and math learning disabilities (Powell, 2011). As more and more students with learning disabilities are included in the general education classroom, teachers must differentiate instruction to benefit all learners. The current strategies emphasized in textbooks are misleading and too general for students who struggle (Jitendra, 2008). Schema-based instruction is an alternative problem solving strategy, which requires students to identify the underlying structure (schema) which each word problem belongs, to translate important information to a diagram, and then to solve the problem. This project uses cognitive theory as a theoretical framework and analyzes the effects of schema-based instruction on students with learning disabilities and their general education peers. Enhancement materials for implementing schema-based instruction were created so that teachers in a small, urban, parochial school could meet the mathematical needs of a diverse population of students. The key features of the enhancement materials include descriptions of each schema, directions for delivering explicit instruction, example and practice word problems, and student reference materials/manipulatives

Can acquisition of expertise be supported by technology?

Professional trainees in the workplace are increasingly required to demonstrate specific standards of competence. Yet, empirical evidence of how professionals acquire competence in practice is lacking. The danger, then, is that efforts to support learning processes may be misguided. We hypothesised that a systemic view of how expertise is acquired would support more timely and appropriate development of technology to support workplace learning. The aims of this study were to provide an empirically based understanding of workplace learning and explore how learning could be facilitated through suitable application of technology. We have used the medical specialist trainee as an exemplar of how professionals acquire expertise within a complex working environment. We describe our methodological approach, based on the amalgam of systems analysis and qualitative research methods. We present the development of a framework for analysis and early findings from qualitative data analysis. Based on our findings so far, we present a tentative schema representing how technology can support learning with suggestions for the types of technology that could be used