9,767 research outputs found
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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Reasoning About Pragmatics with Neural Listeners and Speakers
We present a model for pragmatically describing scenes, in which contrastive
behavior results from a combination of inference-driven pragmatics and learned
semantics. Like previous learned approaches to language generation, our model
uses a simple feature-driven architecture (here a pair of neural "listener" and
"speaker" models) to ground language in the world. Like inference-driven
approaches to pragmatics, our model actively reasons about listener behavior
when selecting utterances. For training, our approach requires only ordinary
captions, annotated _without_ demonstration of the pragmatic behavior the model
ultimately exhibits. In human evaluations on a referring expression game, our
approach succeeds 81% of the time, compared to a 69% success rate using
existing techniques
A Cognitive Model for Conversation
International audienceThis paper describes a symbolic model of rational action and decision making to support analysing dialogue. The model approximates principles of behaviour from game theory, and its proof theory makes Gricean principles of cooperativity derivable when the agentsâ preferences align
Modeling Boundedly Rational Agents with Latent Inference Budgets
We study the problem of modeling a population of agents pursuing unknown
goals subject to unknown computational constraints. In standard models of
bounded rationality, sub-optimal decision-making is simulated by adding
homoscedastic noise to optimal decisions rather than explicitly simulating
constrained inference. In this work, we introduce a latent inference budget
model (L-IBM) that models agents' computational constraints explicitly, via a
latent variable (inferred jointly with a model of agents' goals) that controls
the runtime of an iterative inference algorithm. L-IBMs make it possible to
learn agent models using data from diverse populations of suboptimal actors. In
three modeling tasks -- inferring navigation goals from routes, inferring
communicative intents from human utterances, and predicting next moves in human
chess games -- we show that L-IBMs match or outperform Boltzmann models of
decision-making under uncertainty. Inferred inference budgets are themselves
meaningful, efficient to compute, and correlated with measures of player skill,
partner skill and task difficulty
Emergence of Grounded Compositional Language in Multi-Agent Populations
By capturing statistical patterns in large corpora, machine learning has
enabled significant advances in natural language processing, including in
machine translation, question answering, and sentiment analysis. However, for
agents to intelligently interact with humans, simply capturing the statistical
patterns is insufficient. In this paper we investigate if, and how, grounded
compositional language can emerge as a means to achieve goals in multi-agent
populations. Towards this end, we propose a multi-agent learning environment
and learning methods that bring about emergence of a basic compositional
language. This language is represented as streams of abstract discrete symbols
uttered by agents over time, but nonetheless has a coherent structure that
possesses a defined vocabulary and syntax. We also observe emergence of
non-verbal communication such as pointing and guiding when language
communication is unavailable
Efficient Communication via Reinforcement Learning
Why do languages partition mental concepts into words the way the do? Recent works have taken a information-theoretic view on human language and suggested that it is shaped by the need for efficient communication. This means that human language is shaped by a simultaneous pressure for being informative, while also being simple in order to minimize the cognitive load. In this thesis we combine the information-theoretic perspective on language with recent advances in deep multi-agent reinforcement learning. We explore how efficient communication emerges between two artificial agents in a signaling game as a by-product of them maximizing a shared reward signal. This is tested in the domain of colors and numeral systems, two domains in which human languages tends to support efficient communication. We find that the communication developed by the artificial agents in these domains shares characteristics with human languages when it comes to efficiency and structure of semantic partitions. even though the agents lack the full perceptual and linguistic architecture of humans.Our results offer a computational learning perspective that may complement the information-theoretic view on the structure of human languages. The results also suggests that reinforcement learning is a powerful and flexible framework that can be used to test and generate hypotheses in silico
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