5,636 research outputs found
Informational drives for sensor evolution
© 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
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
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
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
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
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
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
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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