84,897 research outputs found
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor
The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities
Towards adaptive multi-robot systems: self-organization and self-adaptation
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible
Naming Game on Adaptive Weighted Networks
We examine a naming game on an adaptive weighted network. A weight of
connection for a given pair of agents depends on their communication success
rate and determines the probability with which the agents communicate. In some
cases, depending on the parameters of the model, the preference toward
successfully communicating agents is basically negligible and the model behaves
similarly to the naming game on a complete graph. In particular, it quickly
reaches a single-language state, albeit some details of the dynamics are
different from the complete-graph version. In some other cases, the preference
toward successfully communicating agents becomes much more relevant and the
model gets trapped in a multi-language regime. In this case gradual coarsening
and extinction of languages lead to the emergence of a dominant language,
albeit with some other languages still being present. A comparison of
distribution of languages in our model and in the human population is
discussed.Comment: 22 pages, accepted in Artificial Lif
The implications of embodiment for behavior and cognition: animal and robotic case studies
In this paper, we will argue that if we want to understand the function of
the brain (or the control in the case of robots), we must understand how the
brain is embedded into the physical system, and how the organism interacts with
the real world. While embodiment has often been used in its trivial meaning,
i.e. 'intelligence requires a body', the concept has deeper and more important
implications, concerned with the relation between physical and information
(neural, control) processes. A number of case studies are presented to
illustrate the concept. These involve animals and robots and are concentrated
around locomotion, grasping, and visual perception. A theoretical scheme that
can be used to embed the diverse case studies will be presented. Finally, we
will establish a link between the low-level sensory-motor processes and
cognition. We will present an embodied view on categorization, and propose the
concepts of 'body schema' and 'forward models' as a natural extension of the
embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of
Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5
Separating Agent-Functioning and Inter-Agent Coordination by Activated Modules: The DECOMAS Architecture
The embedding of self-organizing inter-agent processes in distributed
software applications enables the decentralized coordination system elements,
solely based on concerted, localized interactions. The separation and
encapsulation of the activities that are conceptually related to the
coordination, is a crucial concern for systematic development practices in
order to prepare the reuse and systematic integration of coordination processes
in software systems. Here, we discuss a programming model that is based on the
externalization of processes prescriptions and their embedding in Multi-Agent
Systems (MAS). One fundamental design concern for a corresponding execution
middleware is the minimal-invasive augmentation of the activities that affect
coordination. This design challenge is approached by the activation of agent
modules. Modules are converted to software elements that reason about and
modify their host agent. We discuss and formalize this extension within the
context of a generic coordination architecture and exemplify the proposed
programming model with the decentralized management of (web) service
infrastructures
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