11 research outputs found
NTCCRT: A concurrent constraint framework for soft-real time music interaction
Writing music interaction systems is not easy because their concurrent processes usually access shared resources in a non-deterministic order, often leading to unpredictable behavior. Using Pure Data (Pure Data) and Max/MSP, it is possible to program concurrency; however, it is difficult to synchronize processes based on multiple criteria. Process calculi such as the Non-deterministic Timed Concurrent Constraint (ntcc) calculus, overcome that problem by representing, declaratively, the synchronization of multiple criteria as constraints. In this article, we propose the framework Ntccrt, as a new alternative to manage concurrency in Pure Data and Max/MSP. Ntccrt is a real-time capable interpreter for ntcc. Using Ntccrt binary plugins in Pure Data, we executed models for machine improvisation and signal processing. We also analyzed two case studies: one of a machine improvisation system and one of a signal processing system. We found out that performance of both case studies is compatible with soft real-time music interaction; it means, a musician can interact with Ntccrt without noticeable delays during the interaction
Learning programs by learning from failures
We describe an inductive logic programming (ILP) approach called learning
from failures. In this approach, an ILP system (the learner) decomposes the
learning problem into three separate stages: generate, test, and constrain. In
the generate stage, the learner generates a hypothesis (a logic program) that
satisfies a set of hypothesis constraints (constraints on the syntactic form of
hypotheses). In the test stage, the learner tests the hypothesis against
training examples. A hypothesis fails when it does not entail all the positive
examples or entails a negative example. If a hypothesis fails, then, in the
constrain stage, the learner learns constraints from the failed hypothesis to
prune the hypothesis space, i.e. to constrain subsequent hypothesis generation.
For instance, if a hypothesis is too general (entails a negative example), the
constraints prune generalisations of the hypothesis. If a hypothesis is too
specific (does not entail all the positive examples), the constraints prune
specialisations of the hypothesis. This loop repeats until either (i) the
learner finds a hypothesis that entails all the positive and none of the
negative examples, or (ii) there are no more hypotheses to test. We introduce
Popper, an ILP system that implements this approach by combining answer set
programming and Prolog. Popper supports infinite problem domains, reasoning
about lists and numbers, learning textually minimal programs, and learning
recursive programs. Our experimental results on three domains (toy game
problems, robot strategies, and list transformations) show that (i) constraints
drastically improve learning performance, and (ii) Popper can outperform
existing ILP systems, both in terms of predictive accuracies and learning
times.Comment: Accepted for the machine learning journa