If you like cellular automata you will like Turmits, even if it is only for the name.
A Turmite is a Turing machine that works over a two-dimensional grid, that is, an agent that moves, reads and writes symbols over the cells of the grid. Its state is an arrow and, depending on the symbol that it reads, it turns to the left or to the right, switching the symbol at the same time. Several symbols are admitted, and the rule is specified by the turning sense that the machine has over each symbol.
Turmites are a generalization of Langton's ants, and they present very complex and diverse behaviors. We prove that any Turmite, except for those whose rule does not depend on the symbol, can simulate any Turing Machine.
We also prove the P-completeness of prediction their future behavior by explicitly giving a log-space reduction from the Topological Circuit Value Problem. A similar result was already established for Langtons ant; here we use a similar technique but prove a stronger notion of simulation, and for a more general family.
You may have heard of swarm intelligence and ant colony algorithms but ants aren't the only creatures capable of exhibiting collective intelligence.
Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization.
This paper has proposed a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired by the whales behavior of communicating with each other via ultrasound for hunting.
The proposed Whale Swarm Algorithm has been compared with several popular metaheuristic algorithms on comprehensive performance metrics. According to the experimental results, Whale Swarm Algorithm has a quite competitive performance when compared with other algorithms.
The only small problem is that a group of whales is called a pod and so it should be the Whale pod algorithm.
You may have seen flocking simulations like boids but what are the rules that actually govern flocking behaviour of real animals?
The striking patterns of collective animal behavior, including ant trails, bird flocks, and fish schools, can result from local interactions among animals without centralized control. Several of these rules of interaction have been proposed, but it has proven difficult to discriminate which ones are implemented in nature.
As a method to better discriminate among interaction rules, we propose to follow the slow birth of a rule of interaction during animal development. Specifically, we followed the development of zebrafish, Danio rerio, and found that larvae turn toward each other from 7 days postfertilization and increase the intensity of interactions until 3 weeks.
This developmental dataset allows testing the parameter-free predictions of a simple rule in which animals attract each other part of the time, with attraction defined as turning toward another animal chosen at random. This rule makes each individual likely move to a high density of conspecifics, and moving groups naturally emerge. Development of attraction strength corresponds to an increase in the time spent in attraction behavior. Adults were found to follow the same attraction rule, suggesting a potential significance for adults of other species.
The paper contains enough details of the simulation to allow you to replicate it.
Google has announced updates to the Responsible Generative AI Toolkit to enable it to be used with any LLM model. The Responsible GenAI Toolkit provides resources to design, build, and evaluate open A [ ... ]
