Modelling and analyzing adaptive self-assembling strategies with Maude

Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify, validate and analyse a prominent example of adaptive system: robot swarms equipped with self-assembly strategies. The analysis exploits the statistical model checker PVeStA

Planning and Proof Planning

. The paper adresses proof planning as a specific AI planning. It describes some peculiarities of proof planning and discusses some possible cross-fertilization of planning and proof planning. 1 Introduction Planning is an established area of Artificial Intelligence (AI) whereas proof planning introduced by Bundy in [2] still lives in its childhood. This means that the development of proof planning needs maturing impulses and the natural questions arise What can proof planning learn from its Big Brother planning?' and What are the specific characteristics of the proof planning domain that determine the answer?'. In turn for planning, the analysis of approaches points to a need of mature techniques for practical planning. Drummond [8], e.g., analyzed approaches with the conclusion that the success of Nonlin, SIPE, and O-Plan in practical planning can be attributed to hierarchical action expansion, the explicit representation of a plan's causal structure, and a very simple form of propo..

Increasing the Students\u27 Writing Skill Through Mind Mapping Technique

This study is classroom action research implementing the use of mind mapping technique to improve the students\u27writing skill. The aim of this study is to identify whether mind mapping technique can improve students\u27 writing skill and describe the classroom situation when mind mapping is used in teaching and learning process of writing skill.The data were collected from 44 students of the first year students of English department at Nusantara PGRI Kediri University. The data compiled from the observation sheets on the lecturer\u27s and students\u27 performance done by the collaborator, field note made by the lecturer,questionnaire on the students and mainly the students\u27 achievement at the cycle test proved the mind mapping technique to be effective in improving the students\u27 writing skill. This study has been done into two cycles. The result of the study shows that the students\u27 mean score improved from the first cycle (70.95) to the second cycle (76.68). And out of 65.91% of the subjects got the target scores 75 in cycle I and it had been reached by 84.08% of the students in cycle II. In short, it can be concluded that in the last cycle, students had really made significant progress. The analyses resulted in the findings that mind mapping technique could improve the students\u27writing skil

Simulating Membrane Systems in Digital Computers

* Work partially supported by contribution of EU commission Under The Fifth Framework Programme, project “MolCoNet” IST-2001-32008.Membrane Computing started with the analogy between some processes produced inside the complex structure of living cells and computational processes. In the same way that in other branches of Natural Computing, the model is extracted from nature but it is not clear whether or not the model must come back to nature to be implemented. As in other cases in Natural Computing: Artificial Neural Networks, Genetic Algorithms, etc; the models have been implemented in digital computers. Hence, some papers have been published considering implementation of Membrane Computing in digital computers. This paper introduces an overview in the field of simulation in Membrane Computing

Learning by Seeing by Doing: Arithmetic Word Problems

Learning by doing in pursuit of real-world goals has received much attention from education researchers but has been unevenly supported by mathematics education software at the elementary level, particularly as it involves arithmetic word problems. In this article, we give examples of doing-oriented tools that might promote children\u27s ability to see significant abstract structures in mathematical situations. The reflection necessary for such seeing is motivated by activities and contexts that emphasize affective and social aspects. Natural language, as a representation already familiar to children, is key in these activities, both as a means of mathematical expression and as a link between situations and various abstract representations. These tools support children\u27s ownership of a mathematical problem and its expression; remote sharing of problems and data; software interpretation of children\u27s own word problems; play with dynamically linked representations with attention to children\u27s prior connections; and systematic problem variation based on empirically determined level of difficulty

The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions