Using Inductive Rule Learning Techniques to Learn Planning Domains

2018 ◽  
pp. 642-656
Author(s):  
José Á. Segura-Muros ◽  
Raúl Pérez ◽  
Juan Fernández-Olivares
Keyword(s):  
Rule Learning ◽  
Learning Techniques ◽  
Inductive Rule Learning

scholarly journals On-The-Fly Syntheziser Programming with Fuzzy Rule Learning

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 969
Author(s):  
Iván Paz ◽  
Àngela Nebot ◽  
Francisco Mugica ◽  
Enrique Romero
Keyword(s):  
Real Time ◽  
Cross Validation ◽  
State Of The Art ◽  
Rule Learning ◽  
Fuzzy Rule ◽  
Feature Space ◽  
Maximum Volume ◽  
Time Variations ◽  
Fuzzy Rule Learning ◽  
Inductive Rule Learning

This manuscript explores fuzzy rule learning for sound synthesizer programming within the performative practice known as live coding. In this practice, sound synthesis algorithms are programmed in real time by means of source code. To facilitate this, one possibility is to automatically create variations out of a few synthesizer presets. However, the need for real-time feedback makes existent synthesizer programmers unfeasible to use. In addition, sometimes presets are created mid-performance and as such no benchmarks exist. Inductive rule learning has shown to be effective for creating real-time variations in such a scenario. However, logical IF-THEN rules do not cover the whole feature space. Here, we present an algorithm that extends IF-THEN rules to hyperrectangles, which are used as the cores of membership functions to create a map of the input space. To generalize the rules, the contradictions are solved by a maximum volume heuristics. The user controls the novelty-consistency balance with respect to the input data using the algorithm parameters. The algorithm was evaluated in live performances and by cross-validation using extrinsic-benchmarks and a dataset collected during user tests. The model’s accuracy achieves state-of-the-art results. This, together with the positive criticism received from live coders that tested our methodology, suggests that this is a promising approach.

scholarly journals An Empirical Investigation Into Deep and Shallow Rule Learning

2021 ◽  
Vol 4 ◽  
Author(s):  
Florian Beck ◽  
Johannes Fürnkranz
Keyword(s):  
Learning Algorithm ◽  
State Of The Art ◽  
Rule Learning ◽  
Learning Rule ◽  
Disjunctive Normal Form ◽  
Universal Function ◽  
Point Of View ◽  
Positive Class ◽  
Rule Sets ◽  
Inductive Rule Learning

Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theories, all state-of-the-art learners still learn descriptions that directly relate the input features to the target concept. In the simplest case, concept learning, this is a disjunctive normal form (DNF) description of the positive class. While it is clear that this is sufficient from a logical point of view because every logical expression can be reduced to an equivalent DNF expression, it could nevertheless be the case that more structured representations, which form deep theories by forming intermediate concepts, could be easier to learn, in very much the same way as deep neural networks are able to outperform shallow networks, even though the latter are also universal function approximators. However, there are several non-trivial obstacles that need to be overcome before a sufficiently powerful deep rule learning algorithm could be developed and be compared to the state-of-the-art in inductive rule learning. In this paper, we therefore take a different approach: we empirically compare deep and shallow rule sets that have been optimized with a uniform general mini-batch based optimization algorithm. In our experiments on both artificial and real-world benchmark data, deep rule networks outperformed their shallow counterparts, which we take as an indication that it is worth-while to devote more efforts to learning deep rule structures from data.

Inductive rule learning on the knowledge level

2011 ◽  
Vol 12 (3-4) ◽  
pp. 237-248 ◽  
Author(s):  
Ute Schmid ◽  
Emanuel Kitzelmann
Keyword(s):  
Rule Learning ◽  
Knowledge Level ◽  
Inductive Rule Learning

The Use of Prescription Drugs and Drugs of Abuse for Neuroenhancement in Europe

2015 ◽  
Vol 20 (3) ◽  
pp. 155-166 ◽  
Author(s):  
Larissa J. Maier ◽  
Michael P. Schaub
Keyword(s):  
Prescription Drugs ◽  
Illicit Drugs ◽  
Adverse Reactions ◽  
Low Dose ◽  
Drugs Of Abuse ◽  
Mental Performance ◽  
Or Education ◽  
Learning Techniques ◽  
Monitoring The Future ◽  
Definition Of

Abstract. Pharmacological neuroenhancement, defined as the misuse of prescription drugs, illicit drugs, or alcohol for the purpose of enhancing cognition, mood, or prosocial behavior, is not widespread in Europe – nevertheless, it does occur. Thus far, no drug has been proven as safe and effective for cognitive enhancement in otherwise healthy individuals. European studies have investigated the misuse of prescription and illicit stimulants to increase cognitive performance as well as the use of tranquilizers, alcohol, and cannabis to cope with stress related to work or education. Young people in educational settings report pharmacological neuroenhancement more frequently than those in other settings. Although the regular use of drugs for neuroenhancement is not common in Europe, the irregular and low-dose usage of neuroenhancers might cause adverse reactions. Previous studies have revealed that obtaining adequate amounts of sleep and using successful learning techniques effectively improve mental performance, whereas pharmacological neuroenhancement is associated with ambiguous effects. Therefore, non-substance-related alternatives should be promoted to cope with stressful situations. This paper reviews the recent research on pharmacological neuroenhancement in Europe, develops a clear definition of the substances used, and formulates recommendations for practitioners regarding how to react to requests for neuroenhancement drug prescriptions. We conclude that monitoring the future development of pharmacological neuroenhancement in Europe is important to provide effective preventive measures when required. Furthermore, substance use to cope with stress related to work or education should be studied in depth because it is likely more prevalent and dangerous than direct neuroenhancement.

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