1,339 research outputs found
Local structure helps learning optimized automata in recurrent neural networks
Deterministic behavior can be modeled conveniently in the framework of finite automata. We present a recurrent neural network model based on biologically plausible circuit motifs that can learn deterministic transition models from given input sequences. Furthermore, we introduce simple structural constraints on the connectivity that are inspired by biology. Simulation results show that this leads to great improvements in terms of training time, and efficient use of resources in the converged system. Previous work has shown how specific instances of finite-state machines (FSMs) can be synthesized in recurrent neural networks by interconnecting multiple soft winner-take-all (SWTA) circuits - small circuits that can faithfully reproduce many computational properties of cortical networks. We extend this framework with a reinforcement learning mechanism to learn correct state transitions as input and reward signals are provided. Not only does the network learn a model for the observed sequences, and encode it in the recurrent synaptic weights, it also finds solutions that are close-to-optimal in the number of states required to model the target system, leading to efficient scaling behavior as the size of the target problems increases
Explaining Aha! moments in artificial agents through IKE-XAI: Implicit Knowledge Extraction for eXplainable AI
During the learning process, a child develops a mental representation of the task he or she is learning.
A Machine Learning algorithm develops also a latent representation of the task it learns. We investigate
the development of the knowledge construction of an artificial agent through the analysis of its
behavior, i.e., its sequences of moves while learning to perform the Tower of HanoĂŻ (TOH) task. The TOH
is a well-known task in experimental contexts to study the problem-solving processes and one of the
fundamental processes of children’s knowledge construction about their world. We position ourselves
in the field of explainable reinforcement learning for developmental robotics, at the crossroads of
cognitive modeling and explainable AI. Our main contribution proposes a 3-step methodology named
Implicit Knowledge Extraction with eXplainable Artificial Intelligence (IKE-XAI) to extract the implicit
knowledge, in form of an automaton, encoded by an artificial agent during its learning. We showcase
this technique to solve and explain the TOH task when researchers have only access to moves that
represent observational behavior as in human–machine interaction. Therefore, to extract the agent
acquired knowledge at different stages of its training, our approach combines: first, a Q-learning
agent that learns to perform the TOH task; second, a trained recurrent neural network that encodes
an implicit representation of the TOH task; and third, an XAI process using a post-hoc implicit rule
extraction algorithm to extract finite state automata. We propose using graph representations as visual
and explicit explanations of the behavior of the Q-learning agent. Our experiments show that the IKEXAI
approach helps understanding the development of the Q-learning agent behavior by providing
a global explanation of its knowledge evolution during learning. IKE-XAI also allows researchers to
identify the agent’s Aha! moment by determining from what moment the knowledge representation
stabilizes and the agent no longer learns.Region BretagneEuropean Union via the FEDER programSpanish Government Juan de la Cierva Incorporacion - MCIN/AEI IJC2019-039152-IGoogle Research Scholar Gran
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
Reservoir computing (RC), first applied to temporal signal processing, is a
recurrent neural network in which neurons are randomly connected. Once
initialized, the connection strengths remain unchanged. Such a simple structure
turns RC into a non-linear dynamical system that maps low-dimensional inputs
into a high-dimensional space. The model's rich dynamics, linear separability,
and memory capacity then enable a simple linear readout to generate adequate
responses for various applications. RC spans areas far beyond machine learning,
since it has been shown that the complex dynamics can be realized in various
physical hardware implementations and biological devices. This yields greater
flexibility and shorter computation time. Moreover, the neuronal responses
triggered by the model's dynamics shed light on understanding brain mechanisms
that also exploit similar dynamical processes. While the literature on RC is
vast and fragmented, here we conduct a unified review of RC's recent
developments from machine learning to physics, biology, and neuroscience. We
first review the early RC models, and then survey the state-of-the-art models
and their applications. We further introduce studies on modeling the brain's
mechanisms by RC. Finally, we offer new perspectives on RC development,
including reservoir design, coding frameworks unification, physical RC
implementations, and interaction between RC, cognitive neuroscience and
evolution.Comment: 51 pages, 19 figures, IEEE Acces
Concepts and Paradigms for Neuromorphic Programming
The value of neuromorphic computers depends crucially on our ability to
program them for relevant tasks. Currently, neuromorphic computers are mostly
limited to machine learning methods adapted from deep learning. However,
neuromorphic computers have potential far beyond deep learning if we can only
make use of their computational properties to harness their full power.
Neuromorphic programming will necessarily be different from conventional
programming, requiring a paradigm shift in how we think about programming in
general. The contributions of this paper are 1) a conceptual analysis of what
"programming" means in the context of neuromorphic computers and 2) an
exploration of existing programming paradigms that are promising yet overlooked
in neuromorphic computing. The goal is to expand the horizon of neuromorphic
programming methods, thereby allowing researchers to move beyond the shackles
of current methods and explore novel directions
Evolutionary Computation Applied to Urban Traffic Optimization
At the present time, many sings seem to indicate that we live a global energy and environmental crisis. The scientific community argues that the global warming process is, at least in some degree, a consequence of modern societies unsustainable development. A key area in that situation is the citizens mobility. World economies seem to require fast and efficient transportation infrastructures for a significant fraction of the population. The non-stopping overload process that traffic networks are suffering calls for new solutions. In the vast majority of cases it is not viable to extend that infrastructures due to costs, lack of available space, and environmental impacts. Thus, traffic departments all around the world are very interested in optimizing the existing infrastructures to obtain the very best service they can provide. In the last decade many initiatives have been developed to give the traffic network new management facilities for its better exploitation. They are grouped in the so called Intelligent Transportation Systems. Examples of these approaches are the Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS). Most of them provide drivers or traffic engineers the current traffic real/simulated situation or traffic forecasts. They may even suggest actions to improve the traffic flow. To do so, researchers have done a lot of work improving traffic simulations, specially through the development of accurate microscopic simulators. In the last decades the application of that family of simulators was restricted to small test cases due to its high computing requirements. Currently, the availability of cheap faster computers has changed this situation. Some famous microsimulators are MITSIM(Yang, Q., 1997), INTEGRATION (Rakha, H., et al., 1998), AIMSUN2 (Barcelo, J., et al., 1996), TRANSIMS (Nagel, K. & Barrett, C., 1997), etc. They will be briefly explained in the following section. Although traffic research is mainly targeted at obtaining accurate simulations there are few groups focused at the optimization or improvement of traffic in an automatic manner â not dependent on traffic engineers experience and âartâ. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co
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