5,636 research outputs found

    Informational drives for sensor evolution

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    © 2012 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) licenseIt has been hypothesized that the evolution of sensors is a pivotal driver for the evolution of organisms, and especially, as a crucial part of the perception-action loop, a driver for cognitive development. The questions of why and how this is the case are important: what are the principles that push the evolution of sensorimotor systems? An interesting aspect of this problem is the co-option of sensors for functions other than those originally driving their development (e.g. the auditive sense of bats being employed as a 'visual' modality). Even more striking is the phenomenon found in nature of sensors being driven to the limits of precision, while starting from much simpler beginnings. While a large potential for diversification and exaptation is visible in the observed phenotypes, gaining a deeper understanding of why and how this can be achieved is a significant problem. In this present paper, we will introduce a formal and generic information-theoretic model for understanding potential drives of sensor evolution, both in terms of improving sensory ability and in terms of extending and/or shifting sensory function

    Traffic at the Edge of Chaos

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    We use a very simple description of human driving behavior to simulate traffic. The regime of maximum vehicle flow in a closed system shows near-critical behavior, and as a result a sharp decrease of the predictability of travel time. Since Advanced Traffic Management Systems (ATMSs) tend to drive larger parts of the transportation system towards this regime of maximum flow, we argue that in consequence the traffic system as a whole will be driven closer to criticality, thus making predictions much harder. A simulation of a simplified transportation network supports our argument.Comment: Postscript version including most of the figures available from http://studguppy.tsasa.lanl.gov/research_team/. Paper has been published in Brooks RA, Maes P, Artifical Life IV: ..., MIT Press, 199

    Design methodology for smart actuator services for machine tool and machining control and monitoring

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    This paper presents a methodology to design the services of smart actuators for machine tools. The smart actuators aim at replacing the traditional drives (spindles and feed-drives) and enable to add data processing abilities to implement monitoring and control tasks. Their data processing abilities are also exploited in order to create a new decision level at the machine level. The aim of this decision level is to react to disturbances that the monitoring tasks detect. The cooperation between the computational objects (the smart spindle, the smart feed-drives and the CNC unit) enables to carry out functions for accommodating or adapting to the disturbances. This leads to the extension of the notion of smart actuator with the notion of agent. In order to implement the services of the smart drives, a general design is presented describing the services as well as the behavior of the smart drive according to the object oriented approach. Requirements about the CNC unit are detailed. Eventually, an implementation of the smart drive services that involves a virtual lathe and a virtual turning operation is described. This description is part of the design methodology. Experimental results obtained thanks to the virtual machine are then presented

    Big Data Privacy Context: Literature Effects On Secure Informational Assets

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    This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.Comment: 21 pages, 9 figure

    Informational Constraints and Organisation of Behaviour

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    Based on the view of an agent as an information processing system, and the premise that for such a system it is evolutionary advantageous to be parsimonious with respect to informational burden, an information-theoretical framework is set up to study behaviour under information minimisation pressures. This framework is based on the existing method of relevant information, which is adopted and adapted to the study of a range of cognitive aspects. Firstly, the model of a simple reactive actor is extended to include layered decision making and a minimal memory, in which it is shown that these aspects can decrease some form of bandwidth requirements in an agent, but at the cost of an increase at a different stage or moment in time, or for the system as a whole. However, when combined, they do make it possible to operate with smaller bandwidths at each part of the cognitive system, without increasing the bandwidth of the whole or lowering performance. These results motivate the development of the concept of look-ahead information, which extends the relevant information method to include time, and future informational effects of immediate actions in a more principled way. It is shown that this concept can give rise to intrinsic drives to avoid uncertainty, simplify the environment, and develop a predictive memory. Next, the framework is extended to incorporate a set of goals, rather than deal with just a single task. This introduces the task description as a new source of relevant information, and with that the concept of relevant goal information. Studying this quantity results in several observations: minimising goal information bandwidth results in ritualised behaviour; relevant goal and state information may to some point be exchanged for one another without affecting the agent’s performance; the dynamics of goal information give rise to a natural notion of sub-goals; bottlenecks on goal memory, and a measure of efficiency on the use of these bottlenecks, provide natural abstractions of the environment, and a global reference frame that supersedes local features of the environment. Finally, it is shown how an agent or species could actually arrive at having a large repertoire of goals and accompanying optimal sensors and behaviour, while under a strong information-minimisation pressure. This is done by introducing an informational model of sensory evolution, which indicates that a fundamental information-theoretical law may underpin an important evolutionary catalyst; namely, even a fully minimal sensor can carry additional information, dubbed here concomitant information, that is required to unlock the actual relevant information, which enables a minimal agent to still explore, enter and acquire different niches, accelerating a possible evolution to higher acuity and behavioural abilities

    Empowerment and State-dependent Noise : An Intrinsic Motivation for Avoiding Unpredictable Agents

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    Empowerment is a recently introduced intrinsic motivation algorithm based on the embodiment of an agent and the dynamics of the world the agent is situated in. Computed as the channel capacity from an agent’s actuators to an agent’s sensors, it offers a quantitative measure of how much an agent is in control of the world it can perceive. In this paper, we expand the approximation of empowerment as a Gaussian linear channel to compute empowerment based on the covariance matrix between actuators and sensors, incorporating state dependent noise. This allows for the first time the study of continuous systems with several agents. We found that if the behaviour of another agent cannot be predicted accurately, then interacting with that agent will decrease the empowerment of the original agent. This leads to behaviour realizing collision avoidance with other agents, purely from maximising an agent’s empowermentFinal Accepted Versio

    Omnipresent Maxwell’s demons orchestrate information management in living cells

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    The development of synthetic biology calls for accurate understanding of the critical functions that allow construction and operation of a living cell. Besides coding for ubiquitous structures, minimal genomes encode a wealth of functions that dissipate energy in an unanticipated way. Analysis of these functions shows that they are meant to manage information under conditions when discrimination of substrates in a noisy background is preferred over a simple recognition process. We show here that many of these functions, including transporters and the ribosome construction machinery, behave as would behave a material implementation of the informationmanaging agent theorized by Maxwell almost 150 years ago and commonly known as Maxwell’s demon (MxD). A core gene set encoding these functions belongs to the minimal genome required to allow the construction of an autonomous cell. These MxDs allow the cell to perform computations in an energy-efficient way that is vastly better than our contemporary computers

    Interaction Histories and Short-Term Memory: Enactive Development of Turn-Taking Behaviours in a Childlike Humanoid Robot

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    In this article, an enactive architecture is described that allows a humanoid robot to learn to compose simple actions into turn-taking behaviours while playing interaction games with a human partner. The robot’s action choices are reinforced by social feedback from the human in the form of visual attention and measures of behavioural synchronisation. We demonstrate that the system can acquire and switch between behaviours learned through interaction based on social feedback from the human partner. The role of reinforcement based on a short-term memory of the interaction was experimentally investigated. Results indicate that feedback based only on the immediate experience was insufficient to learn longer, more complex turn-taking behaviours. Therefore, some history of the interaction must be considered in the acquisition of turn-taking, which can be efficiently handled through the use of short-term memory.Peer reviewedFinal Published versio
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