Adaptive Intelligent Tutoring System for learning Computer Theory

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according to the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned

Overview on agent-based social modelling and the use of formal languages

Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft

A Theory of Formal Synthesis via Inductive Learning

Formal synthesis is the process of generating a program satisfying a high-level formal specification. In recent times, effective formal synthesis methods have been proposed based on the use of inductive learning. We refer to this class of methods that learn programs from examples as formal inductive synthesis. In this paper, we present a theoretical framework for formal inductive synthesis. We discuss how formal inductive synthesis differs from traditional machine learning. We then describe oracle-guided inductive synthesis (OGIS), a framework that captures a family of synthesizers that operate by iteratively querying an oracle. An instance of OGIS that has had much practical impact is counterexample-guided inductive synthesis (CEGIS). We present a theoretical characterization of CEGIS for learning any program that computes a recursive language. In particular, we analyze the relative power of CEGIS variants where the types of counterexamples generated by the oracle varies. We also consider the impact of bounded versus unbounded memory available to the learning algorithm. In the special case where the universe of candidate programs is finite, we relate the speed of convergence to the notion of teaching dimension studied in machine learning theory. Altogether, the results of the paper take a first step towards a theoretical foundation for the emerging field of formal inductive synthesis

Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis

Even with impressive advances in automated formal methods, certain problems in system verification and synthesis remain challenging. Examples include the verification of quantitative properties of software involving constraints on timing and energy consumption, and the automatic synthesis of systems from specifications. The major challenges include environment modeling, incompleteness in specifications, and the complexity of underlying decision problems. This position paper proposes sciduction, an approach to tackle these challenges by integrating inductive inference, deductive reasoning, and structure hypotheses. Deductive reasoning, which leads from general rules or concepts to conclusions about specific problem instances, includes techniques such as logical inference and constraint solving. Inductive inference, which generalizes from specific instances to yield a concept, includes algorithmic learning from examples. Structure hypotheses are used to define the class of artifacts, such as invariants or program fragments, generated during verification or synthesis. Sciduction constrains inductive and deductive reasoning using structure hypotheses, and actively combines inductive and deductive reasoning: for instance, deductive techniques generate examples for learning, and inductive reasoning is used to guide the deductive engines. We illustrate this approach with three applications: (i) timing analysis of software; (ii) synthesis of loop-free programs, and (iii) controller synthesis for hybrid systems. Some future applications are also discussed

Experience applying language processing techniques to develop educational software that allow active learning methodologies by advising students

This paper is focused on those systems that allow students to build their own knowledge by providing them with feedback regarding their actions while performing a problem based learning activity or while making changes to problem statements, so that a higher order thinking skill can be achieved. This feedback is the consequence of an automatic assessment. Particularly, we propose a method that makes use of Language Processor techniques for developing these kinds of systems. This method could be applied in subjects in which problem statements and solutions can be formalized by mean of a formal language and the problems can be solved in an algorithmic way. The method has been used to develop a number of tools that are partially described in this paper. Thus, we show that our approach is applicable in addressing the development of the aforementioned systems. One of these tools (a virtual laboratory for language processing) has been in use for several years in order to support home assignments. The data collected for these years are presented and analyzed in this paper. The results of the analysis confirm that this tool is effective in facilitating the achievement of learning outcomes

Non-Deterministic Computational Thinking: Challenges and Opportunities

An educational paradigm that has improved problem-solving capacity is computational thinking, which uses characteristics such as decomposition, abstraction, pattern recognition, and algorithmic thinking. However, most of the resources developed under this paradigm are deterministic. However, the current world is not linear. Non-deterministic dynamics play a vital role in today\u27s world. Decisions about the same fact can cause different events, and students must be prepared to live with such uncertainties. This article discusses challenges and possibilities in the development of non-deterministic computational thinking resources. This work shows a large field of research yet to be worked on, with new possibilities and a great potential to connect new resources with the students\u27 daily lives