The phrase "autonomous interacting" is correct and usable in written English.
It can be used to describe entities or systems that operate independently while engaging with one another.
Example: "The research focused on autonomous interacting robots that could collaborate without human intervention."
Alternatives: "independent interaction" or "self-directed engagement".
The ACE model-based method is to study the economics and emissions by evolving systems of autonomous interacting agents [10].
In this paper, we propose a new model checking algorithm aims at verifying systems designed as a set of autonomous interacting agents.
An agent-based system employs a computational model of autonomous interacting agents in an environment with the purpose of assessing the emergent behavior of the group.
The next generation of software systems includes systems composed of a large number of distributed, decentralized, autonomous, interacting, cooperating, organically grown, heterogeneous, and continually evolving subsystems, which we call IT Ecosystems.
An agent can be considered as a computational entity that operates without human intervention (autonomous), interacts with each other (sociality), perceives and reacts to their environment (re-activity), always acts to obtain its aim (rationality) and exhibits goal-oriented behaviour by taking initiatives (pro-activity).
Autonomous robots interacting with human users need to build and continuously update scene representations.
Autonomous robots interacting with humans in an evolving environment need the ability to acquire knowledge from their multiple perceptual channels in an unsupervised way.
Depth images also have other important applications in the assembly and inspection of industrial products, autonomous robots interacting with humans and real objects, intelligent transportation systems, biometric authentication and in biomedical imaging, where they have an important role in compensating for unwanted motion of patients during imaging.
Ethical challenges will unfold as algorithms are developed that impact how humans and autonomous vehicles interact.
These concepts can be applied in the Multi-Agent Systems field where autonomous agents interact in order to solve a common goal.
Agents are able to make autonomous decisions, interact with each other and exhibit proactive behaviour based on their internal goals [ 14].
