34,487 research outputs found
Learning of data structures and algorithms using a distance learning platform
Learning has been considered as a non-trivial human activity because it involves some complex tasks. In distance learning there is an additional complexity due to space and time shift in learning and teaching activities. Computer-based distance learning has an incremental difficulty: the use of technological tools by both learning interaction actors (learner and teacher). These tools are generally grouped in a platform system that is usually designed and implemented under some common rules: to allow the development of instructional material, to manage synchronous and asynchronous dialogues, etc. Unfortunately, in the design of this kind of platforms, knowledge about technological aspects can get more attention than learner's knowledge acquisition processes or teacher's strategies: the resultant platforms frequently are no flexible systems that do not support the plug-in of tutoring systems. In this paper we describe a system aimed to support computer-based distance learning of data structures and algorithms. The learner's understanding of this area of Computer Science depends on integration between dynamical visualization and logical reasoning: the presentation of the domain knowledge should put more emphasis on this dynarnism Finally, we discuss about technological feasibility to plug-in our system in a flexible computer-based distance-learning platform being developed
Asynchronous Multi-Context Systems
In this work, we present asynchronous multi-context systems (aMCSs), which
provide a framework for loosely coupling different knowledge representation
formalisms that allows for online reasoning in a dynamic environment. Systems
of this kind may interact with the outside world via input and output streams
and may therefore react to a continuous flow of external information. In
contrast to recent proposals, contexts in an aMCS communicate with each other
in an asynchronous way which fits the needs of many application domains and is
beneficial for scalability. The federal semantics of aMCSs renders our
framework an integration approach rather than a knowledge representation
formalism itself. We illustrate the introduced concepts by means of an example
scenario dealing with rescue services. In addition, we compare aMCSs to
reactive multi-context systems and describe how to simulate the latter with our
novel approach.Comment: International Workshop on Reactive Concepts in Knowledge
Representation (ReactKnow 2014), co-located with the 21st European Conference
on Artificial Intelligence (ECAI 2014). Proceedings of the International
Workshop on Reactive Concepts in Knowledge Representation (ReactKnow 2014),
pages 31-37, technical report, ISSN 1430-3701, Leipzig University, 2014.
http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-15056
Sleeping Beauty Reconsidered: Conditioning and Reflection in Asynchronous Systems
A careful analysis of conditioning in the Sleeping Beauty problem is done,
using the formal model for reasoning about knowledge and probability developed
by Halpern and Tuttle. While the Sleeping Beauty problem has been viewed as
revealing problems with conditioning in the presence of imperfect recall, the
analysis done here reveals that the problems are not so much due to imperfect
recall as to asynchrony. The implications of this analysis for van Fraassen's
Reflection Principle and Savage's Sure-Thing Principle are considered.Comment: A preliminary version of this paper appears in Principles of
Knowledge Representation and Reasoning: Proceedings of the Ninth
International Conference (KR 2004). This version will appear in Oxford
Studies in Epistemolog
Integrating DGSs and GATPs in an Adaptative and Collaborative Blended-Learning Web-Environment
The area of geometry with its very strong and appealing visual contents and
its also strong and appealing connection between the visual content and its
formal specification, is an area where computational tools can enhance, in a
significant way, the learning environments.
The dynamic geometry software systems (DGSs) can be used to explore the
visual contents of geometry. This already mature tools allows an easy
construction of geometric figures build from free objects and elementary
constructions. The geometric automated theorem provers (GATPs) allows formal
deductive reasoning about geometric constructions, extending the reasoning via
concrete instances in a given model to formal deductive reasoning in a
geometric theory.
An adaptative and collaborative blended-learning environment where the DGS
and GATP features could be fully explored would be, in our opinion a very rich
and challenging learning environment for teachers and students.
In this text we will describe the Web Geometry Laboratory a Web environment
incorporating a DGS and a repository of geometric problems, that can be used in
a synchronous and asynchronous fashion and with some adaptative and
collaborative features.
As future work we want to enhance the adaptative and collaborative aspects of
the environment and also to incorporate a GATP, constructing a dynamic and
individualised learning environment for geometry.Comment: In Proceedings THedu'11, arXiv:1202.453
Towards formal models and languages for verifiable Multi-Robot Systems
Incorrect operations of a Multi-Robot System (MRS) may not only lead to
unsatisfactory results, but can also cause economic losses and threats to
safety. These threats may not always be apparent, since they may arise as
unforeseen consequences of the interactions between elements of the system.
This call for tools and techniques that can help in providing guarantees about
MRSs behaviour. We think that, whenever possible, these guarantees should be
backed up by formal proofs to complement traditional approaches based on
testing and simulation.
We believe that tailored linguistic support to specify MRSs is a major step
towards this goal. In particular, reducing the gap between typical features of
an MRS and the level of abstraction of the linguistic primitives would simplify
both the specification of these systems and the verification of their
properties. In this work, we review different agent-oriented languages and
their features; we then consider a selection of case studies of interest and
implement them useing the surveyed languages. We also evaluate and compare
effectiveness of the proposed solution, considering, in particular, easiness of
expressing non-trivial behaviour.Comment: Changed formattin
A Method to Identify and Analyze Biological Programs through Automated Reasoning.
Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function
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