Synergy emergence in deep reinforcement motor learning

Human motor control has always been efficient at executing complex movements naturally, efficiently, and without much thought involved. This is because of the existence of motor synergy in the central nervous system (CNS). ...

Computer Sciences

Researchers sniff out AI breakthroughs in mammal brains

When you smell an orange, the scent is most likely combined with several others: car exhaust, garbage, flowers, soap. Those smells bind simultaneously to the hundreds of receptors in your brain's olfactory bulb, obscuring ...


A flower pollination algorithm for efficient robot path planning

Over the past decade or so, researchers worldwide have developed increasingly advanced techniques to enable robot navigation in a variety of environments, including on land, in the air, underwater or on particularly rough ...

Hi Tech & Innovation

Goodyear's biodegradable concept tire regenerates its tread

Goodyear recently unveiled a tire concept that could revolutionize the auto industry. Dubbed reCharge, this concept tire would never require replacements or rotations because it regenerates its tread as needed.


Researchers develop flooding prediction tool

By incorporating the architecture of city drainage systems and readings from flood gauges into a comprehensive statistical framework, researchers at Texas A&M University can now accurately predict the evolution of floods ...


Swarming robots avoid collisions, traffic jams

For self-driving vehicles to become an everyday reality, they need to safely and flawlessly navigate one another without crashing or causing unnecessary traffic jams.

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In mathematics, computing, linguistics, and related subjects, an algorithm is a finite sequence of instructions, an explicit, step-by-step procedure for solving a problem, often used for calculation and data processing. It is formally a type of effective method in which a list of well-defined instructions for completing a task, will when given an initial state, proceed through a well-defined series of successive states, eventually terminating in an end-state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as probabilistic algorithms, incorporate randomness.

A partial formalization of the concept began with attempts to solve the Entscheidungsproblem (the "decision problem") posed by David Hilbert in 1928. Subsequent formalizations were framed as attempts to define "effective calculability" (Kleene 1943:274) or "effective method" (Rosser 1939:225); those formalizations included the Gödel-Herbrand-Kleene recursive functions of 1930, 1934 and 1935, Alonzo Church's lambda calculus of 1936, Emil Post's "Formulation 1" of 1936, and Alan Turing's Turing machines of 1936–7 and 1939.

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