scholarly journals Creating and Capturing Artificial Emotions in Autonomous Robots and Software Agents

2021 ◽  
Author(s):  
Claus Hoffmann ◽  
Pascal Linden ◽  
Maria-Esther Vidal
Keyword(s):  
Decision Making ◽  
Control System ◽  
Autonomous Agents ◽  
Autonomous Robots ◽  
Software Agents ◽  
Software Agent ◽  
Knowledge Graph ◽  
Artificial Emotions ◽  
Underlying Mechanisms ◽  
Agent Knowledge

This paper presents ARTEMIS, a control system for autonomous robots or software agents. ARTEMIS can create human-like artificial emotions during interactions with their environment. We describe the underlying mechanisms for this. The control system also captures its past artificial emotions. A specific interpretation of a knowledge graph, called an Agent Knowledge Graph, stores these artificial emotions. ARTEMIS then utilizes current and stored emotions to adapt decision making and planning processes. As proof of concept, we realize a concrete software agent based on the ARTEMIS control system. This software agent acts as a user assistant and executes their orders and instructions. The environment of this user assistant consists of several other autonomous agents that offer their services. The execution of a user’s orders requires interactions of the user assistant with these autonomous service agents. These interactions lead to the creation of artificial emotions within the user assistant. The first experiments show that it is possible to realize an autonomous user assistant with plausible artificial emotions with ARTEMIS and record these artificial emotions in its Agent Knowledge Graph. The results also show that captured emotions support successful planning and decision making in complex dynamic environments. The user assistant with emotions surpasses an emotionless version of the user assistant.

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.

Intelligent Agents and Autonomous Robots

2017 ◽  
pp. 275-283 ◽  
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.

Decision Making in Intelligent Agents

2011 ◽  
pp. 431-436
Author(s):  
Mats Danielson ◽  
Love Ekenberg
Keyword(s):  
Decision Making ◽  
Intelligent Agents ◽  
Software Agents ◽  
Dynamic Environment ◽  
Software Agent ◽  
Software Systems ◽  
Distributed Software ◽  
Distributed Components ◽  
Independent Decision ◽  
Severe Uncertainty

There are several ways of building complex distributed software systems, for example in the form of software agents. But regardless of the form, there are some common problems having to do with specification contra execution. One of the problems is the inherent dynamics in the environment many systems are exposed to. The properties of the environment are not known with any precision at the time of construction. This renders a specification of the system incomplete by definition. A traditional software agent is only prepared to handle situations conceived of and implemented at compile-time. Even though it can operate in varying contexts, its decision making abilities are static. One remedy is to prepare the distributed components for a truly dynamic environment, i.e. an environment with changing and somewhat unpredictable conditions. A rational software agent needs both a representation of a decision problem at hand and means for evaluation. AI has traditionally addressed some parts of this problem such as representation and reasoning, but has hitherto to a lesser degree addressed the decision making abilities of independent distributed software components (Ekenberg, 2000a, 2000b). Such decision making often has to be carried out under severe uncertainty regarding several parameters. Thus, methods for independent decision making components should be able to handle uncertainties on the probabilities and utilities involved. They have mostly been studied as means of representation, but are now being developed into functional theories of decision making suitable for dynamic use by software agents and other dynamic distributed components. Such a functional theory will also benefit analytical decision support systems intended to aid humans in their decision making. Thus, the generic term agent below stands for a dynamic software component as well as a human or a group of humans assisted by intelligent software.

A developed software agent-knowledge-assisted procurement management tool for retailing enterprise

2019 ◽  
Vol 49 (1) ◽  
pp. 54-75 ◽  
Author(s):  
Bokolo Anthony Jnr
Keyword(s):  
Software Agents ◽  
Software Agent ◽  
Management Tool ◽  
Future Research ◽  
Acceptance Test ◽  
Quantitative Methodology ◽  
Procurement Management ◽  
Content Type ◽  
Knowledge Based ◽  
Agent Knowledge

PurposeThis study aims to develop a software agent-knowledge procurement management tool to address uncertainties from external and internal environments, such as record failure, slow logistics auditing and distribution delay toward improving procurement management in retailing enterprises.Design/methodology/approachQuantitative methodology was used to collect data using a self-administered survey from randomly selected procurement staffs, marketers and customers to measure their perception regarding the feasibility and acceptance of the implemented agent-knowledge-based procurement management tool.FindingsResults from empirical analysis reveal that the implemented tool facilitates collaboration and interaction among buyers, sellers and procurement managers toward enhancing procurement managers’ flexibility to handle unexpected exceptions. In addition, results confirm the feasibility of the implemented tool in supporting procurement management toward handling inventory failure exception, which occurs in traditional procurement approaches. Moreover, descriptive results from user acceptance test verify that the tool was accepted by the respondents.Research limitations/implicationsThe limitation of this study is that the implemented tool is evaluated using data collected from respondents in Malaysia retailing enterprise only; thus, the results cannot be generalized to other enterprises and country. In addition, research implications from this study design a methodological and comprehensive software agent-knowledge-based model that support buyers, sellers and procurement managers with information to facilitate buying and selling operations.Practical implicationsPractically, the designed software agent-knowledge-based model describes how software agents collaborate with each other to facilitate procurement tasks and also use the knowledge base in the implemented tool to provide information sharing platform that manages the dynamics of procurement operations.Social implicationsThis research integrates software agents which are autonomous programs that carryout pre-defined task on behalf of end users. Socially, this study would be useful for procurement managers in developing mechanisms for instilling insights in retailing operations.Originality/valueThis research is among the first to attempt to develop a software agent-knowledge-based model to support procurement management in the retailing enterprise domain. It contributes to promote e-procurement practices by implementing a software agent-knowledge-oriented tool to address uncertainties experienced in retailing enterprise. It is envisaged that this study will provide basis for future research into e-procurement practices for retailing businesses in Malaysia and beyond.

Truth Matters: Cognitive Integrity as an Intervention for Self-Deception

2017 ◽  
Author(s):  
Eugenia Isabel Gorlin ◽  
Michael W. Otto
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Autonomous Agents ◽  
Therapeutic Interventions ◽  
Empirical Validation ◽  
The Past ◽  
Adaptive Problem

To live well in the present, we take direction from the past. Yet, individuals may engage in a variety of behaviors that distort their past and current circumstances, reducing the likelihood of adaptive problem solving and decision making. In this article, we attend to self-deception as one such class of behaviors. Drawing upon research showing both the maladaptive consequences and self-perpetuating nature of self-deception, we propose that self-deception is an understudied risk and maintaining factor for psychopathology, and we introduce a “cognitive-integrity”-based approach that may hold promise for increasing the reach and effectiveness of our existing therapeutic interventions. Pending empirical validation of this theoretically-informed approach, we posit that patients may become more informed and autonomous agents in their own therapeutic growth by becoming more honest with themselves.

Research on sports aided teaching and training decision system oriented to deep convolutional neural network

2021 ◽  
pp. 1-15
Author(s):  
Qinyu Mei ◽  
Ming Li
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Control System ◽  
Convolutional Neural Network ◽  
Force Control ◽  
Control Experiment ◽  
Deep Convolutional Neural Network ◽  
Flexible Cable ◽  
Cable Force ◽  
And Training

Aiming at the construction of the decision-making system for sports-assisted teaching and training, this article first gives a deep convolutional neural network model for sports-assisted teaching and training decision-making. Subsequently, In order to meet the needs of athletes to assist in physical exercise, a squat training robot is built using a self-developed modular flexible cable drive unit, and its control system is designed to assist athletes in squatting training in sports. First, the human squat training mechanism is analyzed, and the overall structure of the robot is determined; second, the robot force servo control strategy is designed, including the flexible cable traction force planning link, the lateral force compensation link and the establishment of a single flexible cable passive force controller; In order to verify the effect of robot training, a single flexible cable force control experiment and a man-machine squat training experiment were carried out. In the single flexible cable force control experiment, the suppression effect of excess force reached more than 50%. In the squat experiment under 200 N, the standard deviation of the system loading force is 7.52 N, and the dynamic accuracy is above 90.2%. Experimental results show that the robot has a reasonable configuration, small footprint, stable control system, high loading accuracy, and can assist in squat training in physical education.

Redisign of the Air Traffic Control System-Moving People through Distributed Management

1978 ◽  
Vol 22 (1) ◽  
pp. 485-485
Author(s):  
John G. Kreifeldt
Keyword(s):  
Decision Making ◽  
Control System ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Negative Impact ◽  
Air Traffic ◽  
Critical Function ◽  
Congressional Hearings ◽  
The Future ◽  
Traffic Control System

The present national Air Traffic Control system is a ground-centralized, man intensive system which through design allows relatively little meaningful pilot participation in decision making. The negative impact of this existing design can be measured in delays, dollars and lives. The FAA's design plans for the future ATC system will result in an even more intensive ground-centralized system with even further reduction of pilot decision making participation. In addition, controllers will also be removed from on-line decision making through anticipated automation of some or all of this critical function. Recent congressional hearings indicate that neither pilots nor controllers are happy or sanguine regarding the FAA's design for the future ATC system.

Decision-making system for stock exchange market using artificial emotions

2015 ◽  
Vol 42 (20) ◽  
pp. 7070-7083 ◽  
Author(s):  
Daniel Cabrera-Paniagua ◽  
Claudio Cubillos ◽  
Rosa Vicari ◽  
Enrique Urra
Keyword(s):  
Decision Making ◽  
Stock Exchange ◽  
Exchange Market ◽  
Artificial Emotions ◽  
Decision Making System

Drilling Data Quality Management: Case Study With a Laboratory Scale Drilling Rig

2018 ◽  
Author(s):  
Suranga C. H. Geekiyanage ◽  
Dan Sui ◽  
Bernt S. Aadnoy
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Control System ◽  
Data Analysis ◽  
Quality Management ◽  
Data Quality ◽  
Incomplete Data ◽  
Laboratory Scale ◽  
Data Quality Management ◽  
Drilling Rig

Drilling industry operations heavily depend on digital information. Data analysis is a process of acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that the decision-making, actions executing, events detecting and incident managing of a system can be handled in an efficient and certain manner. This paper aims to provide an approach to understand, cleanse, improve and interpret the post-well or realtime data to preserve or enhance data features, like accuracy, consistency, reliability and validity. Data quality management is a process with three major phases. Phase I is an evaluation of pre-data quality to identify data issues such as missing or incomplete data, non-standard or invalid data and redundant data etc. Phase II is an implementation of different data quality managing practices such as filtering, data assimilation, and data reconciliation to improve data accuracy and discover useful information. The third and final phase is a post-data quality evaluation, which is conducted to assure data quality and enhance the system performance. In this study, a laboratory-scale drilling rig with a control system capable of drilling is utilized for data acquisition and quality improvement. Safe and efficient performance of such control system heavily relies on quality of the data obtained while drilling and its sufficient availability. Pump pressure, top-drive rotational speed, weight on bit, drill string torque and bit depth are available measurements. The data analysis is challenged by issues such as corruption of data due to noises, time delays, missing or incomplete data and external disturbances. In order to solve such issues, different data quality improvement practices are applied for the testing. These techniques help the intelligent system to achieve better decision-making and quicker fault detection. The study from the laboratory-scale drilling rig clearly demonstrates the need for a proper data quality management process and clear understanding of signal processing methods to carry out an intelligent digitalization in oil and gas industry.

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