Nouvelle Artificial Intelligence: Fundamentals and Applications for Producing Robots With Intelligence Levels Similar to Insects

By Fouad Sabry

What Is Nouvelle Artificial Intelligence

In the 1980s, Rodney Brooks, who at the time worked as part of the artificial intelligence laboratory at MIT, laid the groundwork for what is now known as nouvelle artificial intelligence (AI), a methodology for artificial intelligence. New AI is a departure from traditional AI in that its objective is to endow robots with intelligence levels comparable to that of insects. Instead than relying on the created worlds that symbolic AIs generally needed to have programmed into them, researchers believe that intelligence can arise naturally from simple behaviors as these intelligences interact with the "real world."

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Nouvelle AI

Chapter 2: Artificial intelligence

Chapter 3: Subsumption architecture

Chapter 4: Cog (project)

Chapter 5: Behavior-based robotics

Chapter 6: Rodney Brooks

Chapter 7: Neats and scruffies

Chapter 8: Physical symbol system

Chapter 9: Embodied cognitive science

Chapter 10: Situated approach (artificial intelligence)

(II) Answering the public top questions about nouvelle artificial intelligence.

(III) Real world examples for the usage of nouvelle artificial intelligence in many fields.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of nouvelle artificial intelligence.

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• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jun 22, 2023

Book preview

Chapter 1: Nouvelle AI

Rodney Brooks, a member of the MIT artificial intelligence laboratory in the 1980s, is widely credited as the creator of the nouvelle AI approach to AI.

The early robots Shakey and Freddy illustrate the contrasts between real AI and symbolic AI. These robots have a symbolic representation of their local environments stored within. Therefore, this symbolic framework required periodic updating as the robot relocated or the external environment evolved.

Shakey's planning software analyzed the program's architecture and deconstructed it into manageable chunks. Typically, Shakey moved at a glacial pace since this level of calculation took so long to process.

Problems in updating, searching, and generally manipulating the symbolic worlds inside their AIs have long troubled symbolic AI researchers. In place of relying on any kind of internal representation of the environment, sensors are constantly cited by a nouvelle system. When necessary, it uses sensory input to analyze data about the external environment. The world is the greatest model there is, fully updated and accurate at all times, as Brooks puts it.

One of the key tenets of modern AI is the concept that simpler behaviors may evolve into more sophisticated ones. Simple behaviors may consist of things like go ahead and don't hit anything Chasing a moving item is a complicated behavior that might be produced by a robot utilizing nouvelle AI by combining basic behaviors like collision avoidance and moving toward a moving object.

The frame problem is a description of a difficulty in expressing information about a robot in the world using first-order logic (FOL). To utilize standard FOL to represent the state of a robot, numerous axioms (symbolic language) are needed to suggest that things about an environment do not alter arbitrary.

By not overloading the AI or robot with symbolic language, as is done in traditional approaches to AI, Nouvelle AI hopes to circumvent the frame issue and instead allow for the emergence of more complex behaviors via the combination of simpler behavioral aspects.

In the past, artificial intelligence research focused on creating mindless machines that could only communicate with humans via a computer. Newer forms of artificial intelligence, however, aim to create bodily intelligence that can function in the physical environment. Brooks uses Turing's short descriptions of the situated method from 1948 and 1950 to nod in approval. Using a method that would follow the usual training of a kid, Turing imagined a computer equipped with the greatest sense organs that money can buy and taught to understand and speak English. In contrast to this strategy, the others emphasized theoretical pursuits like chess.

To this end, Brooks set out to develop robots that mimicked the behavior of common insects while also eliminating some of the more conventional AI features. Allen and Herbert are two insectoid robots he built.

The environment was not modeled internally in Brooks' insectoid robots. For instance, Herbert deleted most of the data it collected from its sensors and never kept it for more than two seconds.

Allen, named for Allen Newell, was equipped with three autonomous behavior-generating modules and a ring of twelve ultrasonic sonars for primary sensing. These parts were designed to steer clear of both still and moving obstacles. Allen, equipped with simply this module, waited in the center of a room until something came at him, and then he sprinted away while avoiding obstacles.

The robot Herbert, named after Herbert A. Simon, was equipped with a laser system and infrared sensors to gather 3D data at a range of around 12 feet. A variety of basic sensors were stored in Herbert's hand. An apparently goal-oriented activity, the robot's hunt for empty Coke cans and subsequent removal from the AI lab's bustling offices and workplaces, arose as a consequence of the combination of 15 basic behavior components. Simon drew a connection between the ant's complex journey and the complexity of its surroundings, arguing that the former explains the latter better.

Genghis and Squirt were two other robots created by Brooks' group. Genghis could travel across tough terrain and follow a person since he had six legs. Squirt's programming instructed it to wait in silence until it heard a noise, at which point it would start to move toward the sound.

With Brooks's agreement, the topic of whether or not achieving the amount of complexity shown in actual insects is a feasible aim for new AI was raised.

While Von Neumann argued that theorists who select the human nervous system as their model are unrealistically picking 'the most complicated object under the sun,' and that there is little advantage in selecting instead the ant, since any nervous system at all exhibits exceptional complexity, Brooks' own recent work has taken the opposite direction.

In the 1990s, Brooks and Lynn Andrea Stein created a humanoid robot they named Cog with the intention of achieving human levels of intelligence. Cog is a robot having several sensors, a face, and limbs (among other characteristics) that enable it to interact with the environment, learn from its experiences, and build intelligence in the Turing test-described way.

The researchers behind Cog anticipated that it would be intelligent enough to pick up new skills on its own, draw conclusions about the world based on the data it gathered from its senses, and behave accordingly. All construction on the site halted in 2003.

{End Chapter 1}

Chapter 2: Artificial intelligence

As contrast to the natural intelligence exhibited by animals, including humans, artificial intelligence (AI) refers to the intelligence demonstrated by robots. Research in artificial intelligence (AI) has been described as the area of study of intelligent agents, which refers to any system that senses its surroundings and performs actions that optimize its possibility of attaining its objectives. In other words, AI research is a discipline that studies intelligent agents. The term AI impact refers to the process by which activities that were formerly thought to need intelligence but are no longer included in the concept of artificial intelligence as technology advances. AI researchers have adapted and incorporated a broad variety of approaches for addressing issues, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics, in order to tackle these difficulties. Computer science, psychology, linguistics, philosophy, and a great many other academic disciplines all contribute to the development of AI.

The theory that human intellect can be so accurately characterized that a computer may be constructed to imitate it was the guiding principle behind the establishment of this discipline. This sparked philosophical debates concerning the mind and the ethical implications of imbuing artificial organisms with intellect comparable to that of humans; these are topics that have been investigated by myth, literature, and philosophy ever since antiquity.

In ancient times, artificial creatures with artificial intelligence were used in