Showing posts with label AI system. Show all posts
Showing posts with label AI system. Show all posts

AI Glossary - What Is ARIS?


ARIS is a commercially available artificial intelligence system that aids in the assignment of airport gates to inbound planes.

It assigns airport gates and provides an overall perspective of current operations to human decision makers using rule-based reasoning, constraint propagation, and spatial planning.

Using Ascent Technology's fully-integrated From Touchdown to Takeoff® cloud-hosted service, ARIS is a part of the SmartAirport Operations Center solution allows you to deploy human and physical resources on the ground to maximum advantage, even in the face of inevitable delays to your operations.

The SmartAirport Information Manager tools let you to codify and modify your business information, store it in a secure yet flexible repository, and share it throughout your company to facilitate collaborative decision-making. 

They also allow you to develop, amend, and manage flight schedules that drive resource allocation choices, as well as connect the SmartAirport Operations Center to other systems. 

ARIS/SmartBase airport database (AODB), ARIS/SmartBus communication middleware, ARIS/Reports data analyzer, ARIS/SL schedule loader, ARIS/SB schedule builder, and ARIS/BIS billing-information system are some of the company's most popular products.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

AI Glossary - Algorithm And AI Algorithms.


A methodology or procedure for resolving certain issues.

In Artificial Intelligence based systems and applications, an AI algorithm is used. 

An AI algorithm is a subset of machine learning that instructs the computer on how to learn to work independently. 

As a result, the AI system continues to learn in order to optimize procedures and do jobs more quickly.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

Be sure to refer to the complete & active AI Terms Glossary here.

You may also want to read more about Artificial Intelligence here.

Artificial Intelligence - What Is The Monte Carlo Simulation Approach?


Monte Carlo is a simulation approach that uses numerous runs of a nondeterministic simulation based on a random number generator to solve complicated problems.

Deterministic approaches solve equations or systems of equations to arrive at a fixed answer, which is the same every time the computation is conducted.

Monte Carlo techniques, on the other hand, employ a random number generator to choose distinct pathways, resulting in a varied solution each time.

When the deterministic equations are unknown, there are a large number of variables, and the issue is probabilistic in nature, Monte Carlo techniques are applied.

Games of chance, nuclear simulations, quantum effects difficulties, and weather forecasting are all examples of situations where Monte Carlo techniques are routinely used.

Monte Carlo techniques are often employed in machine learning and memory simulations in artificial intelligence to produce more robust responses and to depict, for example, how memory evolves.

Because each Monte Carlo simulation yields just one potential result, the simulation must be repeated hundreds to millions of times in order to generate a probability distribution, which is the overall answer.

Compared to deterministic approaches, Monte Carlo methods may be much more computationally demanding.

Monte Carlo is a popular AI technique for games like checkers, chess, and Go.

These games (particularly Go) contain a high number of potential moves at each stage.

Monte Carlo tree search is a methodology that use the MC method to continually play the game while making a random move at each stage.

The AI system eventually learns the optimum moves for a given game circumstance.

Monte Carlo tree search AIs have a proven track record of beating other AI gaming algorithms on a regular basis.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Emergent Gameplay and Non-Player Characters.

References & Further Reading:

Andrieu, Christophe, Nando de Freitas, Arnaud Doucet, and Michael I. Jordan. 2003. “An Introduction to MCMC for Machine Learning.” Machine Learning 50: 5–43.

Eckhard, Roger. 1987. “Stan Ulam, John von Neumann, and the Monte Carlo Method.” Los Alamos Science 15 (Special Issue): 131–37.

Fu, Michael C. 2018. “Monte Carlo Tree Search: A Tutorial.” In Proceedings of the 2018 Winter Simulation Conference, edited by M. Rabe, A. A. Juan, N. Mustafee, A. Skoogh, S. Jain, and B. Johansson, 222–36. Piscataway, NJ: IEEE.

What Is Artificial General Intelligence?

Artificial General Intelligence (AGI) is defined as the software representation of generalized human cognitive capacities that enables the ...