scholarly journals AVANCES EN INTERACCIÓN HOMBRE – MÁQUINA

2019 ◽  
pp. 29-36
Keyword(s):  
Formal System ◽  
Knowledge Bases ◽  
Protective Mechanism ◽  
New Paradigm ◽  
Formal Systems ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Palabras Clave ◽  
Software Engineers ◽  
Machine Interaction

AVANCES EN INTERACCIÓN HOMBRE – MÁQUINA NEW ADVANCES IN MAN – MACHINE INTERACTION Fernando Ramírez - Icaza. Manuel Villavicencio 1331, Lima -14, Perú. DOI: https://doi.org/10.33017/RevECIPeru2010.0005/ RESUMEN En el presente artículo de investigación se pone énfasis en las diferentes formas de Interacción Hombre – Máquina, las cuales se masificarán, en un futuro no muy lejano, a través de Robots Físicos, Robots Virtuales, u órdenes procedentes del cerebro humano procesadas por electrodos capaces de captar la energía propia del proceso bioquímico entre las neuronas y por ende leer el pensamiento humano. Los robots durante la interacción Hombre – Máquina deben constantemente tomar decisiones para lo cual es importante tener en consideración en la construcción de las Bases de Conocimientos los siguientes pasos: análisis de la información, análisis de la representación de la información sobre la base de criterios taxonómicos, identificación de axiomas, sugerencia de mecanismo de protección de axiomas, sugerencia de mecanismo de inferencia, evaluación de Sistemas Formales, recomendación del Sistema Formal más conveniente y expresivo para la representación de cada uno de los axiomas, recomendación al equipo de Ingenieros de Software el mecanismo más idóneo para la incorporación de axiomas y reglas. Finalmente, se expone las conclusiones y reflexiones del autor sobre este nuevo paradigma de las Ciencias Computacionales. Palabras clave: Inteligencia Artificial, Interacción Hombre - Máquina, Robots, Bases de Conocimientos, Agentes Inteligentes de Software o Softbots, Verbots. ABSTRACT This article emphasizes research on different forms of interaction between man - machine, which will widely spread out in a not too distant future through physical robots, virtual robots, or orders issued by the human brain processed by electrodes capable of capturing the energy of the biochemical process between neurons and thus read human thought. Robots during Man – Machine interaction must constantly make decisions. Therefore, when building knowledge bases it is important to consider the following steps: analysis of data, analysis of the representation of information based on taxonomic criteria, identification of axioms, suggested protective mechanism of axioms, inference mechanism suggestion, evaluation of formal systems, recommendation of formal systems, recommendation of the most convenient and expressive formal system for the representation of each of the axioms, recommendation to the team of software engineers of the most appropriate mechanism for incorporating axioms & rules. Finally, it draws conclusions and reflections of the author on this new paradigm of Computer Sciences. Keywords: Artificial Intelligence, Interaction Man - Machine, Robots, Knowledge Bases, Intelligent Software Agents or Softbots, Verbots.

scholarly journals Identifying research gaps: A review of virtual patient education and self-management

2021 ◽  
pp. 1-13
Author(s):  
Elke Brucker-Kley ◽  
Ulla Kleinberger ◽  
Thomas Keller ◽  
Jonas Christen ◽  
Anita Keller-Senn ◽  
...  
Keyword(s):  
Patient Education ◽  
Health Care Professionals ◽  
Research Agenda ◽  
Interdisciplinary Teams ◽  
New Paradigm ◽  
Human Machine Interaction ◽  
Intelligent Software ◽  
Communication Task ◽  
Intelligent Software Agents ◽  
Computer Scientists

BACKGROUND: Avatars in Virtual Reality (VR) can not only represent humans, but also embody intelligent software agents that communicate with humans, thus enabling a new paradigm of human-machine interaction. OBJECTIVE: The research agenda proposed in this paper by an interdisciplinary team is motivated by the premise that a conversation with a smart agent avatar in VR means more than giving a face and body to a chatbot. Using the concrete communication task of patient education, this research agenda is rather intended to explore which patterns and practices must be constructed visually, verbally, para- and nonverbally between humans and embodied machines in a counselling context so that humans can integrate counselling by an embodied VR smart agent into their thinking and acting in one way or another. METHODS: The scientific literature in different bibliographical databases was reviewed. A qualitative narrative approach was applied for analysis. RESULTS: A research agenda is proposed which investigates how recurring consultations of patients with healthcare professionals are currently conducted and how they could be conducted with an embodied smart agent in immersive VR. CONCLUSIONS: Interdisciplinary teams consisting of linguists, computer scientists, visual designers and health care professionals are required which need to go beyond a technology-centric solution design approach. Linguists’ insights from discourse analysis drive the explorative experiments to identify test and discover what capabilities and attributes the smart agent in VR must have, in order to communicate effectively with a human being.

Modality and folk psychology

2019 ◽  
Vol 28 (1) ◽  
pp. 19-27
Author(s):  
Ja. O. Petik
Keyword(s):  
Modal Logic ◽  
Brain Activity ◽  
Formal System ◽  
Philosophy Of Logic ◽  
Psychological States ◽  
Formal Systems ◽  
Modern Psychology ◽  
Philosophical Interpretation ◽  
Important Direction

The connection of the modern psychology and formal systems remains an important direction of research. This paper is centered on philosophical problems surrounding relations between mental and logic. Main attention is given to philosophy of logic but certain ideas are introduced that can be incorporated into the practical philosophical logic. The definition and properties of basic modal logic and descending ones which are used in study of mental activity are in view. The defining role of philosophical interpretation of modality for the particular formal system used for research in the field of psychological states of agents is postulated. Different semantics of modal logic are studied. The hypothesis about the connection of research in cognitive psychology (semantics of brain activity) and formal systems connected to research of psychological states is stated.

scholarly journals An Analysis of the Interaction Between Intelligent Software Agents and Human Users

2018 ◽  
Vol 28 (4) ◽  
pp. 735-774 ◽  
Author(s):  
Christopher Burr ◽  
Nello Cristianini ◽  
James Ladyman
Keyword(s):  
Software Agents ◽  
Intelligent Software ◽  
Intelligent Software Agents

scholarly journals AN INTEGRATED FRAMEWORK UTILISING SOFTWARE AGENT REASONING AND ONTOLOGY MODELS FOR SENSOR BASED BUILDING MONITORING

2015 ◽  
Vol 21 (3) ◽  
pp. 356-375 ◽  
Author(s):  
Michael Dibley ◽  
Haijiang Li ◽  
Yacine Rezgui ◽  
John Miles
Keyword(s):  
Decision Making ◽  
Sensor Network ◽  
Explicit Knowledge ◽  
Knowledge Bases ◽  
Software Agent ◽  
Facility Management ◽  
Good Opportunity ◽  
Integrated Framework ◽  
Intelligent Software ◽  
Building Monitoring

Smart building monitoring demands a new software infrastructure that can elaborate building domain knowledge in order to provide advanced and intelligent functionalities. Conventional facility management (FM) software tools lack semantically rich components, and that limits the capability of supporting software for automatic information sharing, resource negotiation and to assist in timely decision making. Recent hardware innovation on compact ZigBee sensor devices, software developments on ontology and intelligent software agent paradigms provide a good opportunity to develop tools that can further improve current FM practices. This paper introduces an integrated framework which includes a ZigBee based sensor network and underlying multi-agent software (MAS) components. Several different types of sensors were integrated with the ZigBee host devices to produce compact multi-functional sensor units. The MAS framework incorporates the belief-desire-intention (BDI) abstraction with ontology support (provided via explicit knowledge bases). The different software agent types have been developed to work with sensor hardware to conduct resource negotiation, to optimize battery utilization, to monitor building space in a non-intrusive way and to reason about its usage through real time ontology model queries. The deployed sensor network shows promising intelligent characteristics, and it has been applied in several on-going research projects as an underlying decision making service. More applications and larger deployments have been planned for future work.

Intelligent Multi-Agent Systems

2009 ◽  
pp. 464-469
Author(s):  
Uros Krcadinac ◽  
Milan Stankovic ◽  
Vitomir Kovanovic ◽  
Jelena Jovanovic
Keyword(s):  
Autonomous Agents ◽  
Research Community ◽  
Object Oriented Programming ◽  
Multi Agent Systems ◽  
Autonomous Action ◽  
Agent Systems ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Object Based ◽  
Multi Agent

Since the AAAI (http://www.aaai.org) Spring Symposium in 1994, intelligent software agents and agentbased systems became one of the most significant and exciting areas of research and development (R&D) that inspired many scientific and commercial projects. In a nutshell, an agent is a computer program that is capable of performing a flexible, autonomous action in typically dynamic and unpredictable domains (Luck, McBurney, Shehory, & Willmott, 2005). Agents emerged as a response of the IT research community to the new data-processing requirements that traditional computing models and paradigms were increasingly incapable to deal with (e.g., the huge and ever-increasing quantities of available data). Agent-oriented R&D has its roots in different disciplines. Undoubtedly, the main contribution to the field of autonomous agents came from artificial intelligence (AI) which is focused on building intelligent artifacts; and if these artifacts sense and act in some environment, then they can be considered agents (Russell & Norvig, 1995). Also, object-oriented programming (Booch, 2004), concurrent object-based systems (Agha, Wegner, & Yonezawa, 1993), and human-computer interaction (Maes, 1994) are fields that have constantly driven forward the agent R&D in the last few decades.

Intelligent Software Agents Analysis in E-Commerce II

2009 ◽  
pp. 1452-1457
Author(s):  
Xin Luo ◽  
Somasheker Akkaladevi
Keyword(s):  
Parallel Processing ◽  
Cognitive Ability ◽  
Software Agents ◽  
Limited Resources ◽  
Information Complexity ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Processing Abilities ◽  
Search For Information

Cowan et al. (2002) argued that the human cognitive ability to search for information and to evaluate their usefulness is extremely limited in comparison to those of computers. In detail, it’s cumbersome and time-consuming for a person to search for information from limited resources and to evaluate the information’s usefulness. They further indicated that while people are able to perform several queries in parallel and are good at drawing parallels and analogies between pieces of information, advanced systems that embody ISA architecture are far more effective in terms of calculation power and parallel processing abilities, particularly in the quantities of material they can process (Cowan et al. 2002). According to Bradshaw (1997), information complexity will continue to increase dramatically in the coming decades. He further contended that the dynamic and distributed nature of both data and applications require that software not merely respond to requests for information but intelligently anticipate, adapt, and actively seek ways to support users.

Mobile Workforce Management in a Service-Oriented Enterprise

2009 ◽  
pp. 283-302
Author(s):  
Dickson K.W. Chiu ◽  
S.C. Cheun ◽  
Ho-Fung Leung
Keyword(s):  
Domain Knowledge ◽  
Software Agents ◽  
Mobile Technologies ◽  
Workforce Management ◽  
Intelligent Software ◽  
Intelligent Software Agents ◽  
Service Oriented ◽  
Business Functions ◽  
Telecommunication Enterprise ◽  
Phased Development

In a service-oriented enterprise, the professional workforce such as salespersons and support staff tends to be mobile with the recent advances in mobile technologies. There are increasing demands for the support of mobile workforce management (MWM) across multiple platforms in order to integrate the disparate business functions of the mobile professional workforce and management with a unified infrastructure, together with the provision of personalized assistance and automation. Typically, MWM involves tight collaboration, negotiation, and sophisticated business-domain knowledge, and thus can be facilitated with the use of intelligent software agents. As mobile devices become more powerful, intelligent software agents can now be deployed on these devices and hence are also subject to mobility. Therefore, a multiagent information-system (MAIS) infrastructure provides a suitable paradigm to capture the concepts and requirements of an MWM as well as a phased development and deployment. In this book chapter, we illustrate our approach with a case study at a large telecommunication enterprise. We show how to formulate a scalable, flexible, and intelligent MAIS with agent clusters. Each agent cluster comprises several types of agents to achieve the goal of each phase of the workforce-management process, namely, task formulation, matchmaking, brokering, commuting, and service.

Intelligent Software Agents in Electronic Commerce

2011 ◽  
pp. 104-112 ◽  
Author(s):  
Mahesh S. Raisinghani ◽  
Christopher Klassen ◽  
Lawrence L. Schkade
Keyword(s):  
Intelligent Agents ◽  
Software Agents ◽  
Intelligent Agent ◽  
Software Agent ◽  
Reasoning Strategies ◽  
Intelligent Software ◽  
Collaborative Behavior ◽  
Intelligent Software Agents ◽  
The Status ◽  
High Level

Although there is no firm consensus on what constitutes an intelligent agent (or software agent), an intelligent agent, when a new task is delegated by the user, should determine precisely what its goal is, evaluate how the goal can be reached in an effective manner, and perform the necessary actions by learning from past experience and responding to unforeseen situations with its adaptive, self-starting, and temporal continuous reasoning strategies. It needs to be not only cooperative and mobile in order to perform its tasks by interacting with other agents but also reactive and autonomous to sense the status quo and act independently to make progress towards its goals (Baek et al., 1999; Wang, 1999). Software agents are goal-directed and possess abilities such as autonomy, collaborative behavior, and inferential capability. Intelligent agents can take different forms, but an intelligent agent can initiate and make decisions without human intervention and have the capability to infer appropriate high-level goals from user actions and requests and take actions to achieve these goals (Huang, 1999; Nardi et al., 1998; Wang, 1999). The intelligent software agent is a computational entity than can adapt to the environment, making it capable of interacting with other agents and transporting itself across different systems in a network.

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