2,981 research outputs found
Personalised correction, feedback, and guidance in an automated tutoring system for skills training
In addition to knowledge, in various domains skills are equally important. Active learning and training are effective forms of education. We present an automated skills training system for a database programming environment that promotes procedural knowledge acquisition
and skills training. The system provides support features such as correction of solutions, feedback and personalised guidance, similar to interactions with a human tutor. Specifically, we address synchronous feedback and guidance based on personalised assessment. Each of these features is automated and includes a level of personalisation and adaptation. At the core of the system is a pattern-based error classification and correction component that analyses
student input
MAT learners for recognizable tree languages and tree series
We review a family of closely related query learning algorithms for unweighted and weighted tree automata, all of which are based on adaptations of the minimal adequate teacher (MAT) model by Angluin. Rather than presenting
new results, the goal is to discuss these algorithms in sufficient detail to make their similarities and differences transparent to the reader interested in grammatical inference of tree automata
Interactive correction and recommendation for computer language learning and training
Active learning and training is a particularly effective form of education. In various domains, skills are equally important to knowledge. We present an automated learning and skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides meaningful, knowledge-level feedback such as correction of student solutions and personalised guidance through recommendations. Specifically, we address automated synchronous feedback and recommendations based on personalised performance assessment. At the core of the tutoring system is a pattern-based error classification and correction component that analyses student input in order to provide immediate feedback and in order to diagnose student weaknesses and suggest further study material. A syntax-driven approach based on grammars and syntax trees provides the solution for a semantic analysis technique. Syntax tree abstractions and comparison techniques based on equivalence rules and pattern matching are specific approaches
Children as Models for Computers: Natural Language Acquisition for Machine Learning
International audienceThis paper focuses on a subfield of machine learning, the so- called grammatical inference. Roughly speaking, grammatical inference deals with the problem of inferring a grammar that generates a given set of sample sentences in some manner that is supposed to be realized by some inference algorithm. We discuss how the analysis and formalization of the main features of the process of human natural language acquisition may improve results in the area of grammatical inference
Editors’ Introduction to [Algorithmic Learning Theory: 18th International Conference, ALT 2007, Sendai, Japan, October 1-4, 2007. Proceedings]
Learning theory is an active research area that incorporates ideas,
problems, and techniques from a wide range of disciplines including
statistics, artificial intelligence, information theory, pattern
recognition, and theoretical computer science. The research reported
at the 18th International Conference on Algorithmic Learning Theory
(ALT 2007) ranges over areas such as unsupervised learning,
inductive inference, complexity and learning, boosting and
reinforcement learning, query learning models, grammatical
inference, online learning and defensive forecasting, and kernel
methods. In this introduction we give an overview of the five
invited talks and the regular contributions of ALT 2007
Parameterized Synthesis with Safety Properties
Parameterized synthesis offers a solution to the problem of constructing
correct and verified controllers for parameterized systems. Such systems occur
naturally in practice (e.g., in the form of distributed protocols where the
amount of processes is often unknown at design time and the protocol must work
regardless of the number of processes). In this paper, we present a novel
learning based approach to the synthesis of reactive controllers for
parameterized systems from safety specifications. We use the framework of
regular model checking to model the synthesis problem as an infinite-duration
two-player game and show how one can utilize Angluin's well-known L* algorithm
to learn correct-by-design controllers. This approach results in a synthesis
procedure that is conceptually simpler than existing synthesis methods with a
completeness guarantee, whenever a winning strategy can be expressed by a
regular set. We have implemented our algorithm in a tool called L*-PSynth and
have demonstrated its performance on a range of benchmarks, including robotic
motion planning and distributed protocols. Despite the simplicity of L*-PSynth
it competes well against (and in many cases even outperforms) the
state-of-the-art tools for synthesizing parameterized systems.Comment: 18 page
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