Principles and Experimentations of Self-organizing Embedded Agents Allowing Learning from Demonstration in Ambient Robotic

Verstaevel, Nicolas; Régis, Christine; Gleizes, Marie-Pierre; Robert, Fabrice

doi:10.1016/j.procs.2015.05.056

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…The AMAS for context learning (AMAS4CL) approach is based on the AMAS theory and more particularly on the Self-Adaptive Context Learning (SACL) [1] paradigm to define the rules of cooperation between agents and proposes a structure composed of several types of agents to explore the space of the problem variables. Algorithms based on the SACL approach are used to solve various problems such as learning by demonstration [20] or Inverse Kinematics [9] in robotics and optimization of the operation of a heat pump [1]. SACL architectures are typically composed of three types of agents:…”

Section: Multi-agent Systemsmentioning

confidence: 99%

How to Solve a Classification Problem Using a Cooperative Tiling Multi-agent System?

Thibault

Verstaevel

Migeon

et al. 2022

Lecture Notes in Computer Science

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Adaptive Multi-Agent Systems (AMAS) transform dynamic problems into problems of local cooperation between agents. We present smapy, an ensemble based AMAS implementation for mobility prediction, whose agents are provided with machine learning models in addition to their cooperation rules. With a detailed methodology, we propose a framework to transform a classification problem into a cooperative tiling of the input variable space. We show that it is possible to use linear classifiers for online non-linear classification on three benchmark toy problems chosen for their different levels of linear separability, if they are integrated in a cooperative Multi-Agent structure. The results obtained show a significant improvement of the performance of linear classifiers in non-linear contexts in terms of classification accuracy and decision boundaries, thanks to the cooperative approach.

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Section: Multi-agent Systemsmentioning

confidence: 99%

How to Solve a Classification Problem Using a Cooperative Tiling Multi-agent System?

Thibault

Verstaevel

Migeon

et al. 2022

Lecture Notes in Computer Science

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“…However, using ML techniques, it is possible to learn this model from an actual HRI. As demonstrated in the figure 1, the HRI can be viewed as a planning problem: in an initial run of the interaction, the robot speech acts are governed by observation, imitation or demonstration techniques [2,21]. One particular approach that seems promising is that of 'beaming' by human pilots [3].…”

Section: Problem Statementmentioning

confidence: 99%

A review of learning planning action models

Arora¹,

Fiorino²,

Pellier³

et al. 2018

The Knowledge Engineering Review

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For social robots to be brought more into widespread use in the fields of companionship, care taking and domestic help, they must be capable of demonstrating social intelligence. In order to be acceptable, they must exhibit socio-communicative skills. Classic approaches to program HRI from observed human-human interactions fails to capture the subtlety of multimodal interactions as well as the key structural differences between robots and humans. The former arises due to a difficulty in quantifying and coding multimodal behaviours, while the latter due to a difference of the degrees of liberty between a robot and a human. However, the notion of reverse engineering from multimodal HRI traces to learn the underlying behavioral blueprint of the robot given multimodal traces seems an option worth exploring. With this spirit, the entire HRI can be seen as a sequence of exchanges of speech acts between the robot and human, each act treated as an action, bearing in mind that the entire sequence is goal-driven. Thus, this entire interaction can be treated as a sequence of actions propelling the interaction from its initial to goal state, also known as a plan in the domain of AI planning. In the same domain, this action sequence that stems from plan execution can be represented as a trace. AI techniques, such as machine learning, can be used to learn behavioral models (also known as symbolic action models in AI), intended to be reusable for AI planning, from the aforementioned multimodal traces. This article reviews recent machine learning techniques for learning planning action models which can be applied to the field of HRI with the intent of rendering robots as socio-communicative.

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“…Through the natural process of demonstration, the user not only shows that the current device behaviour is not satisfying him, but also provides the adequate action to perform. Adaptive Learner by EXperiments (ALEX) [17] is a multi-agent system designed to face this challenge.…”

Section: Alexmentioning

confidence: 99%

“…A good way to design MASs for this type of problems is to decompose the problem following its organisation [13]. For many applications, such as bio-process control [19], engine optimization [3], learning in robotics [17], and user satisfaction in ambient systems [9], this decomposition leads to a crucial sub-problem: mapping the current state of the context with actions and their effects. Context is a word used in many domains and each domain comes with its own definition.…”

Section: Introductionmentioning

confidence: 99%

The Self-Adaptive Context Learning Pattern: Overview and Proposal

Boes

Nigon

Verstaevel

et al. 2015

Modeling and Using Context

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15394The contribution was presented at CONTEXT 2015 :http://cyprusconferences.org/context2015/ Abstract. Over the years, our research group has designed and developed many self-adaptive multi-agent systems to tackle real-world complex problems, such as robot control and heat engine optimization. A recurrent key feature of these systems is the ability to learn how to handle the context they are plunged in, in other words to map the current state of their perceptions to actions and effects. This paper presents the pattern enabling the dynamic and interactive learning of the mapping between context and actions by our multi-agent systems.

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Principles and Experimentations of Self-organizing Embedded Agents Allowing Learning from Demonstration in Ambient Robotic

Cited by 8 publications

References 9 publications

How to Solve a Classification Problem Using a Cooperative Tiling Multi-agent System?

How to Solve a Classification Problem Using a Cooperative Tiling Multi-agent System?

A review of learning planning action models

The Self-Adaptive Context Learning Pattern: Overview and Proposal

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