26 research outputs found
DMVP: Foremost Waypoint Coverage of Time-Varying Graphs
We consider the Dynamic Map Visitation Problem (DMVP), in which a team of
agents must visit a collection of critical locations as quickly as possible, in
an environment that may change rapidly and unpredictably during the agents'
navigation. We apply recent formulations of time-varying graphs (TVGs) to DMVP,
shedding new light on the computational hierarchy of TVG classes by analyzing them in the
context of graph navigation. We provide hardness results for all three classes,
and for several restricted topologies, we show a separation between the classes
by showing severe inapproximability in , limited approximability
in , and tractability in . We also give topologies in
which DMVP in is fixed parameter tractable, which may serve as a
first step toward fully characterizing the features that make DMVP difficult.Comment: 24 pages. Full version of paper from Proceedings of WG 2014, LNCS,
Springer-Verla
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Discovering multi-purpose modules through deep multitask learning
Machine learning scientists aim to discover techniques that can be applied across diverse sets of problems. Such techniques need to exploit regularities that are shared across tasks. This begs the question: What shared regularity is not yet being exploited? Complex tasks may share structure that is difficult for humans to discover. The goal of deep multitask learning is to discover and exploit this structure automatically by training a joint model across tasks. To this end, this dissertation introduces a deep multitask learning framework for collecting generic functional modules that are used in different ways to solve different problems. Within this framework, a progression of systems is developed based on assembling shared modules into task models and leveraging the complementary advantages of gradient descent and evolutionary optimization. In experiments, these systems confirm that modular sharing improves performance across a range of application areas, including general video game playing, computer vision, natural language processing, and genomics; yielding state-of-the-art results in several cases. The conclusion is that multi-purpose modules discovered by deep multitask learning can exceed those developed by humans in performance and generality.Computer Science
Reuse of Neural Modules for General Video Game Playing
A general approach to knowledge transfer is introduced in which an agent
controlled by a neural network adapts how it reuses existing networks as it
learns in a new domain. Networks trained for a new domain can improve their
performance by routing activation selectively through previously learned neural
structure, regardless of how or for what it was learned. A neuroevolution
implementation of this approach is presented with application to
high-dimensional sequential decision-making domains. This approach is more
general than previous approaches to neural transfer for reinforcement learning.
It is domain-agnostic and requires no prior assumptions about the nature of
task relatedness or mappings. The method is analyzed in a stochastic version of
the Arcade Learning Environment, demonstrating that it improves performance in
some of the more complex Atari 2600 games, and that the success of transfer can
be predicted based on a high-level characterization of game dynamics.Comment: Accepted at AAAI 1
Language Model Crossover: Variation through Few-Shot Prompting
This paper pursues the insight that language models naturally enable an
intelligent variation operator similar in spirit to evolutionary crossover. In
particular, language models of sufficient scale demonstrate in-context
learning, i.e. they can learn from associations between a small number of input
patterns to generate outputs incorporating such associations (also called
few-shot prompting). This ability can be leveraged to form a simple but
powerful variation operator, i.e. to prompt a language model with a few
text-based genotypes (such as code, plain-text sentences, or equations), and to
parse its corresponding output as those genotypes' offspring. The promise of
such language model crossover (which is simple to implement and can leverage
many different open-source language models) is that it enables a simple
mechanism to evolve semantically-rich text representations (with few
domain-specific tweaks), and naturally benefits from current progress in
language models. Experiments in this paper highlight the versatility of
language-model crossover, through evolving binary bit-strings, sentences,
equations, text-to-image prompts, and Python code. The conclusion is that
language model crossover is a promising method for evolving genomes
representable as text