112 research outputs found
The Difficulties of Learning Logic Programs with Cut
As real logic programmers normally use cut (!), an effective learning
procedure for logic programs should be able to deal with it. Because the cut
predicate has only a procedural meaning, clauses containing cut cannot be
learned using an extensional evaluation method, as is done in most learning
systems. On the other hand, searching a space of possible programs (instead of
a space of independent clauses) is unfeasible. An alternative solution is to
generate first a candidate base program which covers the positive examples, and
then make it consistent by inserting cut where appropriate. The problem of
learning programs with cut has not been investigated before and this seems to
be a natural and reasonable approach. We generalize this scheme and investigate
the difficulties that arise. Some of the major shortcomings are actually
caused, in general, by the need for intensional evaluation. As a conclusion,
the analysis of this paper suggests, on precise and technical grounds, that
learning cut is difficult, and current induction techniques should probably be
restricted to purely declarative logic languages.Comment: See http://www.jair.org/ for any accompanying file
Special Issue âAI for Cybersecurity: Robust Models for Authentication, Threat and Anomaly Detectionâ
Special Issue "AI for Cybersecurity: Robust Models for Authentication, Threat and Anomaly Detection"
Learning Relations: Basing Top-Down Methods on Inverse Resolution
Abstract Top-down algorithms for relational learning specialize general rules until they are consistent, and are guided by heuristics of different kinds. In general, a correct solution is not guaranteed. By contrast, bottom-up methods are well formalized, usually within the framework of inverse resolution. Inverse resolution has also been used as an efficient tool for deductive reasoning, and here we prove that input refutations can be translated into inverse unit refutations. This result allows us to show that top-down learning methods can be also described by means of inverse resolution, yielding a unified theory of relational learning
A chinrest-based approach to measure eye movements and experimental task engagement in macaques with minimal restraint
\ua9 2024 The AuthorsBackground: The use of Rhesus macaques in vision research is crucial due to their visual system\u27s similarity to humans. While invasive techniques have been the norm, there has been a shift towards non-invasive methods, such as facemasks and head molds, to enhance animal welfare and address ethical concerns. New Method: We present a non-invasive, 3D-printed chinrest with infrared sensors, adapted from canine research, allowing for accurate eye movement measurements and voluntary animal participation in experiments. Results: The chinrest method showed a 16% and 28% increase in average trial numbers for Monkey 1 and Monkey 2, respectively, compared to the traditional headpost method. The engagement was high, with monkeys performing over 500 trials per session and initiating a new trial after an average intertrial interval of approximately 1 second. The hit rate improved by about 10% for Monkey 1 in the chinrest condition, and the fixation precision, measured by the standard deviation of gaze positions, was significantly better in the chinrest condition, with Monkey 1 showing a reduction in fixation imprecision from 0.26\ub0 to 0.17\ub0 in the X-axis. Comparison with Existing Methods: The chinrest approach showed significant improvements in trial engagement and reduction in aborted trials due to fixation breaks, indicating less stress and potentially improved data quality compared to previous non-invasive methods. Conclusions: The chinrest method offers a significant advancement in primate cognitive testing by allowing for precise data collection while addressing animal welfare concerns, possibly leading to better scientific outcomes and a paradigm shift in primate research methodologies
Learning automata with side-effects
Automata learning has been successfully applied in the verification of hardware and software. The size of the automaton model learned is a bottleneck for scalability, and hence optimizations that enable learning of compact representations are important. This paper exploits monads, both as a mathematical structure and a programming construct, to design and prove correct a wide class of such optimizations. Monads enable the development of a new learning algorithm and correctness proofs, building upon a general framework for automata learning based on category theory. The new algorithm is parametric on a monad, which provides a rich algebraic structure to capture non-determinism and other side-effects. We show that this allows us to uniformly capture existing algorithms, develop new ones, and add optimizations
The future of Cybersecurity in Italy: Strategic focus area
This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management
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