87,037 research outputs found
Intelligent Tutoring Systems for Generation Z's Addiction
As generation Z's big data is flooding the Internet through social nets,
neural network based data processing is turning an important cornerstone,
showing significant potential for fast extraction of data patterns. Online
course delivery and associated tutoring are transforming into customizable,
on-demand services driven by the learner. Besides automated grading, strong
potential exists for the development and deployment of next generation
intelligent tutoring software agents. Self-adaptive, online tutoring agents
exhibiting "intelligent-like" behavior, being capable "to learn" from the
learner, will become the next educational superstars. Over the past decade,
computer-based tutoring agents were deployed in a variety of extended reality
environments, from patient rehabilitation to psychological trauma healing. Most
of these agents are driven by a set of conditional control statements and a
large answers/questions pairs dataset. This article provides a brief
introduction on Generation Z's addiction to digital information, highlights
important efforts for the development of intelligent dialogue systems, and
explains the main components and important design decisions for Intelligent
Tutoring System.Comment: 4 page
The evolution of representation in simple cognitive networks
Representations are internal models of the environment that can provide
guidance to a behaving agent, even in the absence of sensory information. It is
not clear how representations are developed and whether or not they are
necessary or even essential for intelligent behavior. We argue here that the
ability to represent relevant features of the environment is the expected
consequence of an adaptive process, give a formal definition of representation
based on information theory, and quantify it with a measure R. To measure how R
changes over time, we evolve two types of networks---an artificial neural
network and a network of hidden Markov gates---to solve a categorization task
using a genetic algorithm. We find that the capacity to represent increases
during evolutionary adaptation, and that agents form representations of their
environment during their lifetime. This ability allows the agents to act on
sensorial inputs in the context of their acquired representations and enables
complex and context-dependent behavior. We examine which concepts (features of
the environment) our networks are representing, how the representations are
logically encoded in the networks, and how they form as an agent behaves to
solve a task. We conclude that R should be able to quantify the representations
within any cognitive system, and should be predictive of an agent's long-term
adaptive success.Comment: 36 pages, 10 figures, one Tabl
Transportable Information Agents
Transportable agents are autonomous programs. They can move through a heterogeneous network of computers under their own control, migrating from host to host. They can sense the state of the network, monitor software conditions, and interact with other agents or resources. The network-sensing tools allow our agents to adapt to the network configuration and to navigate under the control of reactive plans. In this paper we describe the design and implementation of the navigation system that gives our agents autonomy. We also discuss the intelligent and adaptive behavior of autonomous agents in distributed information-gathering tasks
Cultivating intelligent tutoring cognizing agents in ill-defined domains using hybrid approaches
Cognizing agents are those systems that can perceive information from the external environment and can adapt to the changing conditions of that environment. Along the adaptation process a cognizing agent perceives information about the environment and generates reactions. An intelligent tutoring cognizing agent should deal not only with the tutoring system’s world but also with the learner-it should infer and predict new information about the learner and tailor the learning process to fit this specific learner. This paper shows how intelligent tutoring cognizing agents can be cultivated in ill-defined domains using hybrid techniques instantiated in the two example agents AEINS-CA and ALES-CA. These agents offer adaptive learning process and personalized feedback aiming to transfer certain cognitive skills, such as problem solving skills to the learners and develop their reasoning in the two ill-defined domains of ethics and argumentation. The paper focuses on the internal structure of each agent and the reasoning methodology, in which, the cognizing agent administration and construction along with the pedagogical scenarios are described
Towards autonomous system: flexible modular production system enhanced with large language model agents
In this paper, we present a novel framework that combines large language
models (LLMs), digital twins and industrial automation system to enable
intelligent planning and control of production processes. Our approach involves
developing a digital twin system that contains descriptive information about
the production and retrofitting the automation system to offer unified
interfaces of fine-granular functionalities or skills executable by automation
components or modules. Subsequently, LLM-Agents are designed to interpret
descriptive information in the digital twins and control the physical system
through RESTful interfaces. These LLM-Agents serve as intelligent agents within
an automation system, enabling autonomous planning and control of flexible
production. Given a task instruction as input, the LLM-agents orchestrate a
sequence of atomic functionalities and skills to accomplish the task. We
demonstrate how our implemented prototype can handle un-predefined tasks, plan
a production process, and execute the operations. This research highlights the
potential of integrating LLMs into industrial automation systems for more
agile, flexible, and adaptive production processes, while also underscoring the
critical insights and limitations for future work
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