scholarly journals Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 492
Author(s):  
Claudio Urrea ◽  
Felipe Garrido ◽  
John Kern
Keyword(s):  
Intelligent Agents ◽  
Intelligent Agent ◽  
Stereoscopic Vision ◽  
Human Expert ◽  
Reward Function ◽  
Spline Curves ◽  
Control Interface ◽  
Virtual Vehicle ◽  
Torque Curve ◽  
Learning By Imitation

This paper presents the results of the design, simulation, and implementation of a virtual vehicle. Such a process employs the Unity videogame platform and its Machine Learning-Agents library. The virtual vehicle is implemented in Unity considering mechanisms that represent accurately the dynamics of a real automobile, such as motor torque curve, suspension system, differential, and anti-roll bar, among others. Intelligent agents are designed and implemented to drive the virtual automobile, and they are trained using imitation or reinforcement. In the former method, learning by imitation, a human expert interacts with an intelligent agent through a control interface that simulates a real vehicle; in this way, the human expert receives motion signals and has stereoscopic vision, among other capabilities. In learning by reinforcement, a reward function that stimulates the intelligent agent to exert a soft control over the virtual automobile is designed. In the training stage, the intelligent agents are introduced into a scenario that simulates a four-lane highway. In the test stage, instead, they are located in unknown roads created based on random spline curves. Finally, graphs of the telemetric variables are presented, which are obtained from the automobile dynamics when the vehicle is controlled by the intelligent agents and their human counterpart, both in the training and the test track.

scholarly journals Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents

2019 ◽  
Vol 10 (1) ◽  
pp. 24-45
Author(s):  
Samuel Allen Alexander
Keyword(s):  
Structural Properties ◽  
Intelligent Agents ◽  
Intelligent Agent ◽  
Related Question ◽  
Practical Intelligence ◽  
Guiding Principle ◽  
Intelligence Measures ◽  
Open Question ◽  
Structural Theorems

Abstract Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg-Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures.

scholarly journals An intelligent agent model for learning group development in the digital learning environment: A systematic literature review

2020 ◽  
Vol 9 (3) ◽  
pp. 1159-1166
Author(s):  
Budi Laksono Putro ◽  
Yusep Rosmansyah ◽  
Suhardi Suhardi
Keyword(s):  
Literature Review ◽  
Learning Environment ◽  
Systematic Literature Review ◽  
Intelligent Agents ◽  
Intelligent Agent ◽  
Group Development ◽  
Collaborative Problem Solving ◽  
Digital Learning ◽  
Agent Model ◽  
Digital Learning Environment

Group development is the first and most important step for the success of collaborative problem solving (CPS) learning in the digital learning environment (DLE). A literacy study is needed for studies in the intelligent agent domain for group development of collaborative learning in DLE. This paper is a systematic literature review (SLR) of intelligent agents for group formation from 2001 to 2019. This paper aims to find answers to 4 (four) research questions, namely: 1) What components to develop intelligent agents for group development; 2) What is the intelligent agent model for group development; 3) How are the metrics for measuring intelligent agent performance; and 4) How is the Framework for developing intelligent agent. The components of the intelligent agent model consist of: member attributes, group attributes (group constraints), and intelligent techniques. This research refers to Srba and Bielikova's group development model. The stages of the model are formation, performing and closing. An intelligent agent model at the formation stage. A performance metric for the intelligent agent at the performance stage. The framework for developing an intelligent agent is a reference to the stages of development, component selection techniques, and performance measurement of an intelligent agent.

Intelligent Agents and Autonomous Robots

2019 ◽  
pp. 1134-1143
Author(s):  
Deepshikha Bhargava
Keyword(s):  
Language Processing ◽  
Intelligent Agents ◽  
Driving Force ◽  
Autonomous Agents ◽  
New Technologies ◽  
Autonomous Robots ◽  
Software Agents ◽  
Intelligent Agent ◽  
Intelligent Robots ◽  
Systems Software

Over decades new technologies, algorithms and methods are evolved and proposed. We can witness a paradigm shift from typewriters to computers, mechanics to mechnotronics, physics to aerodynamics, chemistry to computational chemistry and so on. Such advancements are the result of continuing research; which is still a driving force of researchers. In the same way, the research in the field of artificial intelligence (Russell, Stuart & Norvig, 2003) is major thrust area of researchers. Research in AI have coined different concepts like natural language processing, expert systems, software agents, learning, knowledge management, robotics to name a few. The objective of this chapter is to highlight the research path from software agents to robotics. This chapter begins with the introduction of software agents. The chapter further progresses with the discussion on intelligent agent, autonomous agents, autonomous robots, intelligent robots in different sections. The chapter finally concluded with the fine line between intelligent agents and autonomous robots.

Affective Agents in E-Learning Platforms

2012 ◽  
pp. 1225-1233
Author(s):  
Christos N. Moridis ◽  
Anastasios A. Economides
Keyword(s):  
Artificial Intelligence ◽  
Learning Environments ◽  
Intelligent Agents ◽  
Intelligent Agent ◽  
Human Learning ◽  
Human Reasoning ◽  
Conversational Agents ◽  
Emotional Aspects ◽  
E Learning ◽  
Learning Platforms

During recent decades there has been an extensive progress towards several Artificial Intelligence (AI) concepts, such as that of intelligent agent. Meanwhile, it has been established that emotions play a crucial role concerning human reasoning and learning. Thus, developing an intelligent agent able to recognize and express emotions has been considered an enormous challenge for AI researchers. Embedding a computational model of emotions in intelligent agents can be beneficial in a variety of domains, including e-learning applications. However, until recently emotional aspects of human learning were not taken into account when designing e-learning platforms. Various issues arise when considering the development of affective agents in e-learning environments, such as issues relating to agents’ appearance, as well as ways for those agents to recognize learners’ emotions and express emotional support. Embodied conversational agents (ECAs) with empathetic behaviour have been suggested to be one effective way for those agents to provide emotional feedback to learners’ emotions. There has been some valuable research towards this direction, but a lot of work still needs to be done to advance scientific knowledge.

Computational Intelligence Foundations and Principles

2017 ◽  
pp. 62-85
Author(s):  
Murugan Sethuraman Sethuraman
Keyword(s):  
Computer Science ◽  
Computational Intelligence ◽  
Intelligent Agents ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Intelligent Agent ◽  
Rational Agents ◽  
Abstract Thought ◽  
Optical Character ◽  
The Brain

AI has been defined in different ways, including the abilities for abstract thought, understanding, communication, reasoning, learning, retaining, planning, and solving. Intelligence is most widely studied in humans, but has also been observed in animals and plants. AI is the intelligence of machines or the simulation of intelligence in machines. AI is both the intelligence of machines and the branch of Computer Science which aims to create it, through the study and design of intelligent agents or rational agents, where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. Achievements include constrained and well-defined problems such as games, crossword-solving and optical character recognition. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception, and the ability to move and manipulate objects. In the field of AI there is no consensus on how closely the brain should be simulated.

Predictability of AI Decisions

2021 ◽  
pp. 17-28
Author(s):  
Grzegorz Musiolik
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Free Will ◽  
Intelligent Agents ◽  
Intelligent Agent ◽  
Mathematical Structure ◽  
General Question ◽  
Safety Issues ◽  
Robotic Applications ◽  
Mathematics And Physics

Artificial intelligence evolves rapidly and will have a great impact on the society in the future. One important question which still cannot be addressed with satisfaction is whether the decision of an intelligent agent can be predicted. As a consequence of this, the general question arises if such agents can be controllable and future robotic applications can be safe. This chapter shows that unpredictable systems are very common in mathematics and physics although the underlying mathematical structure can be very simple. It also shows that such unpredictability can also emerge for intelligent agents in reinforcement learning, especially for complex tasks with various input parameters. An observer would not be capable to distinguish this unpredictability from a free will of the agent. This raises ethical questions and safety issues which are briefly presented.

Secure Agent Roaming for Mobile Business

2009 ◽  
pp. 851-864
Author(s):  
Sheng-Uei Guan
Keyword(s):  
Intelligent Agents ◽  
Online Shopping ◽  
Intelligent Agent ◽  
The Internet ◽  
Primary Concern ◽  
Mobile Business ◽  
Is Security ◽  
Use Of The Internet ◽  
Intelligent Tools ◽  
Open Nature

M-commerce, a new way to conduct business, is gaining more and more popularity due to the wide use of the Internet. Despite its rapid growth, there are limitations that hinder the expansion of m-commerce. The primary concern for online shopping is security. Due to the open nature of the Internet, personal financial details necessary for online shopping can be stolen if sufficient security mechanism is not put in place. How to provide the necessary assurance of security to consumers remains a question mark despite various past efforts. Another concern is the lack of intelligence in locating the correct piece of information. The Internet is an ocean of information depository. It is rich in content but lacks the necessary intelligent tools to help one locate the correct piece of information. Intelligent agent, a piece of software that can act intelligently on behalf of its owner, is designed to fill this gap. However, no matter how intelligent an agent is, its functionality is limited if it remains on its owner’s machine and does not have any roaming capability. With the roaming capability, more security concerns arise. In response to these concerns, SAFE, Secure roaming Agent For E-commerce, is designed to provide secure roaming capability to intelligent agents.

Intelligent Agents in a Trust Environment

2002 ◽  
pp. 98-108
Author(s):  
Rahul Singh ◽  
Mark A. Gill
Keyword(s):  
Distributed System ◽  
Intelligent Agents ◽  
Single Agent ◽  
Intelligent Agent ◽  
The Internet ◽  
Commercial Activity ◽  
Agent Based ◽  
Trust Relationships ◽  
Multi Agent ◽  
Oriented Activity

Intelligent agents and multi-agent technologies are an emerging technology in computing and communications that hold much promise for a wide variety of applications in Information Technology. Agent-based systems range from the simple, single agent system performing tasks such as email filtering, to a very complex, distributed system of multiple agents each involved in individual and system wide goal-oriented activity. With the tremendous growth in the Internet and Internet-based computing and the explosion of commercial activity on the Internet in recent years, intelligent agent-based systems are being applied in a wide variety of electronic commerce applications. In order to be able to act autonomously in a market environment, agents must be able to establish and maintain trust relationships. Without trust, commerce will not take place. This research extends previous work in intelligent agents to include a mechanism for handling the trust relationship and shows how agents can be fully used as intermediaries in commerce.

A Cooperative Communicative Intelligent Agent Model for E-Commerce

2011 ◽  
pp. 208-225
Author(s):  
Ric Jentzsch ◽  
Renzo Gobbin
Keyword(s):  
Information Systems ◽  
Intelligent Agents ◽  
World Wide ◽  
Intelligent Agent ◽  
Business Information Systems ◽  
Agent Model ◽  
Business Operations ◽  
Business Information ◽  
The World ◽  
Modern Business

The complexities of business continue to expand. First technology, then the World Wide Web, ubiquitous commerce, mobile commerce, and who knows. Business information systems need to be able to adjust to these increased complexities, while not creating more problems. Here, we put forth a conceptual model for cooperative communicative intelligent agents that can extend itself to the logical constructs needed by modern business operations today and tomorrow.

Software Agents in Today's Digital Economy

2001 ◽  
pp. 88-100 ◽  
Author(s):  
Mahesh S. Raisinghani
Keyword(s):  
Intelligent Agents ◽  
New Technology ◽  
Software Agents ◽  
Information Filtering ◽  
Intelligent Agent ◽  
Social Dimension ◽  
Economic Changes ◽  
Software Products ◽  
Intelligent Software Agents ◽  
High Level

One of the most discussed topics in the information systems literature today is software agent/intelligent agent technology. Software agents are high-level software abstractions with inherent capabilities for communication, decision making, control, and autonomy. They are programs that perform functions such as information gathering, information filtering, or mediation (running in the background) on behalf of a person or entity. They have several aliases such as agents, bots, chatterbots, databots, intellibots, and intelligent software agents/robots. They provide a powerful mechanism to address complex software engineering problems such as abstraction, encapsulation, modularity, reusability, concurrency, and distributed operations. Much research has been devoted to this topic, and more and more new software products billed as having intelligent agent functionality are being introduced on the market every day. The research that is being done, however, does not wholeheartedly endorse this trend. The current research into intelligent agent software technology can be divided into two main areas: technological and social. The latter area is particularly important since, in the excitement of new and emergent technology, people often forget to examine what impact the new technology will have on people’s lives. In fact, the social dimension of all technology is the driving force and most important consideration of technology itself. This chapter presents a socio-technical perspective on intelligent agents and proposes a framework based on the data lifecycle and knowledge discovery using intelligent agents. One of the key ideas of this chapter is best stated by Peter F. Drucker in Management Challenges for the 21st Century when he suggests that in this period of profound social and economic changes, managers should focus on the meaning of information, not the technology that collects it.

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