407 research outputs found
Learning flexible sensori-motor mappings in a complex network
Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex proble
Learning flexible sensori-motor mappings in a complex network
Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem
Prospection in cognition: the case for joint episodic-procedural memory in cognitive robotics
Prospection lies at the core of cognition: it is the means by which an agent \u2013 a person or a cognitive robot \u2013 shifts its perspective from immediate sensory experience to anticipate future events, be they the actions of other agents or the outcome of its own actions. Prospection, accomplished by internal simulation, requires mechanisms for both perceptual imagery and motor imagery. While it is known that these two forms of imagery
are tightly entwined in the mirror neuron system, we do not yet have an effective model of the mentalizing network which would provide a framework to integrate declarative episodic and procedural memory systems and to combine experiential knowledge with skillful know-how. Such a framework would be founded on joint perceptuo-motor representations. In this paper, we examine the case for this form of representation, contrasting sensory-motor theory with ideo-motor theory, and we discuss how such a framework could be realized by joint episodic-procedural memory. We argue that such a representation framework has several advantages for cognitive robots. Since episodic memory operates by recombining imperfectly recalled past experience, this allows it to simulate new or unexpected events. Furthermore, by virtue of its associative nature, joint episodic-procedural memory allows the internal simulation to be conditioned by current context, semantic memory, and the agent\u2019s value system. Context and semantics constrain the combinatorial explosion of potential perception-action associations and allow effective action selection in the pursuit of goals, while the value system provides
the motives that underpin the agent\u2019s autonomy and cognitive development. This joint episodic-procedural memory framework is neutral regarding the final implementation of these episodic and procedural memories, which can be configured sub-symbolically as associative networks or symbolically as content-addressable image databases and databases of motor-control scripts
A Practical Guide to Studying Emergent Communication through Grounded Language Games
The question of how an effective and efficient communication system can
emerge in a population of agents that need to solve a particular task attracts
more and more attention from researchers in many fields, including artificial
intelligence, linguistics and statistical physics. A common methodology for
studying this question consists of carrying out multi-agent experiments in
which a population of agents takes part in a series of scripted and
task-oriented communicative interactions, called 'language games'. While each
individual language game is typically played by two agents in the population, a
large series of games allows the population to converge on a shared
communication system. Setting up an experiment in which a rich system for
communicating about the real world emerges is a major enterprise, as it
requires a variety of software components for running multi-agent experiments,
for interacting with sensors and actuators, for conceptualising and
interpreting semantic structures, and for mapping between these semantic
structures and linguistic utterances. The aim of this paper is twofold. On the
one hand, it introduces a high-level robot interface that extends the Babel
software system, presenting for the first time a toolkit that provides flexible
modules for dealing with each subtask involved in running advanced grounded
language game experiments. On the other hand, it provides a practical guide to
using the toolkit for implementing such experiments, taking a grounded colour
naming game experiment as a didactic example.Comment: This paper was officially published at the 'Language Learning for
Artificial Agents (L2A2) Symposium' of the 2019 Artificial Intelligence and
Simulation of Behaviour (AISB) Conventio
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning
Re-conceptualising the Language Game Paradigm in the Framework of Multi-Agent Reinforcement Learning
In this paper, we formulate the challenge of re-conceptualising the language
game experimental paradigm in the framework of multi-agent reinforcement
learning (MARL). If successful, future language game experiments will benefit
from the rapid and promising methodological advances in the MARL community,
while future MARL experiments on learning emergent communication will benefit
from the insights and results gained from language game experiments. We
strongly believe that this cross-pollination has the potential to lead to major
breakthroughs in the modelling of how human-like languages can emerge and
evolve in multi-agent systems.Comment: This paper was accepted for presentation at the 2020 AAAI Spring
Symposium `Challenges and Opportunities for Multi-Agent Reinforcement
Learning' after a double-blind reviewing proces
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