The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform
Michael Luck - One of the best experts on this subject based on the ideXlab platform.
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Multi-Agent-Systems and Applications - Motivated Agent Behaviour and Requirements Applied to Virtual Emergencies
Lecture Notes in Computer Science, 2020Co-Authors: Sorabain Wolfheart De Lioncourt, Michael LuckAbstract:Virtual environments provide a rich and varied domain for intelligent Agents, but questions of design and development in this context are still to be answered. An Agent with multiple requirements and limited or constrained resources must be able to make decisions as to how to divide those resources in order to satisfy its requirements. It may not be possible to satisfy all of them at once, so some may have to be sacrificed for the sake of those that are more important; in other cases a compromise may be possible in which all requirements are partially satisfied. This paper examines the kinds of requirements we may expect to have of an Agent in virtual environments, and describes how we can measure an Agent's preformance in this light. Such an analysis can be used as a conceptual design tool and as the basis of an Agent specification. An Agent architecture based on the BDI model is proposed in which design and implementation is decomposed in terms of requirements, and which allows the intuitive development of sophisticated Agents with multiple requirements in a dynamic virtual environment.
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Motivated Agent behaviour and requirements applied to virtual emergencies
Lecture Notes in Computer Science, 2002Co-Authors: Sorabain Wolfheart De Lioncourt, Michael LuckAbstract:Virtual environments provide a rich and varied domain for intelligent Agents, but questions of design and development in this context are still to be answered. An Agent with multiple requirements and limited or constrained resources must be able to make decisions as to how to divide those resources in order to satisfy its requirements. It may not be possible to satisfy all of them at once, so some may have to be sacrificed for the sake of those that are more important; in other cases a compromise may be possible in which all requirements are partially satisfied. This paper examines the kinds of requirements we may expect to have of an Agent in virtual environments, and describes how we can measure an Agent's preformance in this light. Such an analysis can be used as a conceptual design tool and as the basis of an Agent specification. An Agent architecture based on the BDI model is proposed in which design and implementation is decomposed in terms of requirements, and which allows the intuitive development of sophisticated Agents with multiple requirements in a dynamic virtual environment.
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DAI - Engagement and Cooperating in Motivated Agent Modelling
Distributed Artificial Intelligence Architecture and Modelling, 1996Co-Authors: Michael Luck, Mark D'invernoAbstract:The title of this paper suggests two distinct aspects of the models that we propose and consider. The first of these is the modelling of other Agents by Motivated Agents. That is to say that the act of modelling is itself Motivated and constrained by the Agent doing that modelling. The second aspect is that all such models will also be of Motivated Agents. It is not sufficient merely to know what other Agents are like, but also to know why they are like that. This why aspect is what provides the extra information that allows a greater understanding of the interactions between entities in the world, and consequently provides for more resilient Agents capable of effectively dealing with new and unforeseen circumstances in an uncertain world. Previous work has described a formal framework for agency and autonomy in which Agents are viewed as objects with goals, and autonomous Agents are Agents with motivations. This paper considers the nature of cooperation within that framework. We identify distinct kinds of interaction, depending on the nature of the entities involved. In particular, we describe and specify the differences that arise in these interactions which we characterise as engagements of non-autonomous Agents, and cooperation between autonomous Agents.
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engagement and cooperating in Motivated Agent modelling
Proceedings of the First Australian Workshop on DAI: Distributed Artificial Intelligence: Architecture and Modelling, 1995Co-Authors: Michael Luck, Mark DinvernoAbstract:The title of this paper suggests two distinct aspects of the models that we propose and consider. The first of these is the modelling of other Agents by Motivated Agents. That is to say that the act of modelling is itself Motivated and constrained by the Agent doing that modelling. The second aspect is that all such models will also be of Motivated Agents. It is not sufficient merely to know what other Agents are like, but also to know why they are like that. This why aspect is what provides the extra information that allows a greater understanding of the interactions between entities in the world, and consequently provides for more resilient Agents capable of effectively dealing with new and unforeseen circumstances in an uncertain world. Previous work has described a formal framework for agency and autonomy in which Agents are viewed as objects with goals, and autonomous Agents are Agents with motivations. This paper considers the nature of cooperation within that framework. We identify distinct kinds of interaction, depending on the nature of the entities involved. In particular, we describe and specify the differences that arise in these interactions which we characterise as engagements of non-autonomous Agents, and cooperation between autonomous Agents.
Kathryn E Merrick - One of the best experts on this subject based on the ideXlab platform.
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Computational Motivation, Autonomy and Trustworthiness: Can We Have It All?
Foundations of Trusted Autonomy, 2020Co-Authors: Kathryn E Merrick, Adam Klyne, Medria K. D. HardhienataAbstract:Computational motivation—such as curiosity, novelty-seeking, achievement, affiliation and power motivation-facilitates open-ended goal generation by artificial Agents and robots. This further supports diversity, adaptation and cumulative, life-long learning by machines. However, as machines acquire greater autonomy, this may begin to affect human perception of their trustworthiness. Can machines be self-Motivated, autonomous and trustworthy? This chapter examines the impact of self-Motivated autonomy on trustworthiness in the context of intrinsically Motivated Agent swarms.
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Agent models for self Motivated home assistant bots
2009 INTERNATIONAL CONFERNECE ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS‐09), 2010Co-Authors: Kathryn E Merrick, Kamran ShafiAbstract:Modern society increasingly relies on technology to support everyday activities. In the past, this technology has focused on automation, using computer technology embedded in physical objects. More recently, there is an expectation that this technology will not just embed reactive automation, but also embed intelligent, proactive automation in the environment. That is, there is an emerging desire for novel technologies that can monitor, assist, inform or entertain when required, and not just when requested. This paper presents three self‐Motivated, home‐assistant bot applications using different self‐Motivated Agent models. Self‐Motivated Agents use a computational model of motivation to generate goals proactively. Technologies based on self‐Motivated Agents can thus respond autonomously and proactively to stimuli from their environment. Three prototypes of different self‐Motivated Agent models, using different computational models of motivation, are described to demonstrate these concepts.
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Agent Models for Self‐Motivated Home‐Assistant Bots
2010Co-Authors: Kathryn E Merrick, Kamran ShafiAbstract:Modern society increasingly relies on technology to support everyday activities. In the past, this technology has focused on automation, using computer technology embedded in physical objects. More recently, there is an expectation that this technology will not just embed reactive automation, but also embed intelligent, proactive automation in the environment. That is, there is an emerging desire for novel technologies that can monitor, assist, inform or entertain when required, and not just when requested. This paper presents three self‐Motivated, home‐assistant bot applications using different self‐Motivated Agent models. Self‐Motivated Agents use a computational model of motivation to generate goals proactively. Technologies based on self‐Motivated Agents can thus respond autonomously and proactively to stimuli from their environment. Three prototypes of different self‐Motivated Agent models, using different computational models of motivation, are described to demonstrate these concepts.
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EG-ICE - Intrinsically Motivated intelligent sensed environments
Lecture Notes in Computer Science, 2006Co-Authors: Mary Lou Maher, Kathryn E Merrick, Owen MacindoeAbstract:Intelligent rooms comprise hardware devices that support human activities in a room and software that has some level of control over the devices. “Intelligent” implies that the room is considered to behave in an intelligent manner or includes some aspect of artificial intelligence in its implementation. The focus of this paper is intelligent sensed environments, including rooms or interactive spaces that display adaptive behaviour through learning and motivation. We present Motivated Agent models that incorporate machine learning in which the motivation component eliminates the need for a benevolent teacher to prepare problem specific reward functions or training examples. Our model of motivation is based on concepts of “curiosity”, “novelty” and “interest”. We explore the potential for this model through the implementation of a curious place.
Dan P. Mcadams - One of the best experts on this subject based on the ideXlab platform.
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studying the Motivated Agent through time personal goal development during the adult life span
Journal of Personality, 2017Co-Authors: William L. Dunlop, Brittany L. Bannon, Dan P. McadamsAbstract:This research examined the rank-order and mean-level consistency of personal goals at two periods in the adult life span. Personal goal continuity was considered among a group of young adults (N = 145) who reported their goals three times over a 3-year period and among a group of midlife adults (N = 163) who specified their goals annually over a 4-year period. Goals were coded for a series of motive-based (viz., achievement, affiliation, intimacy, power) and domain-based (viz., finance, generativity, health, travel) categories. In both samples, we noted a moderate degree of rank-order consistency across assessment periods. In addition, the majority of goal categories exhibited a high degree of mean-level consistency. The results of this research suggest that (a) the content of goals exhibits a modest degree of rank-order consistency and a substantial degree of mean-level consistency over time, and (b) considering personality continuity and development as manifest via goals represents a viable strategy for personality psychologists.
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Studying the Motivated Agent through time: Personal goal development during the adult lifespan.
Journal of Personality, 2015Co-Authors: William L. Dunlop, Brittany L. Bannon, Dan P. McadamsAbstract:This research examined the rank-order and mean-level consistency of personal goals at two periods in the adult life span. Personal goal continuity was considered among a group of young adults (N = 145) who reported their goals three times over a 3-year period and among a group of midlife adults (N = 163) who specified their goals annually over a 4-year period. Goals were coded for a series of motive-based (viz., achievement, affiliation, intimacy, power) and domain-based (viz., finance, generativity, health, travel) categories. In both samples, we noted a moderate degree of rank-order consistency across assessment periods. In addition, the majority of goal categories exhibited a high degree of mean-level consistency. The results of this research suggest that (a) the content of goals exhibits a modest degree of rank-order consistency and a substantial degree of mean-level consistency over time, and (b) considering personality continuity and development as manifest via goals represents a viable strategy for personality psychologists.
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Tracing Three Lines of Personality Development
Research in Human Development, 2015Co-Authors: Dan P. McadamsAbstract:I would like the interdisciplinary field of human development to move the whole person from the periphery to the center of its inquiry. In doing so, the field would need to focus on three different lines of personality development, reflecting the perspectives of the person as (1) a social actor, (2) a Motivated Agent, and (3) an autobiographical author. The three perspectives bring together many different domains of inquiry in contemporary psychological and social sciences, from research on the genetic and epigenetic roots of dispositional traits to studies of how culture shapes people's self-defining life stories.
Kamran Shafi - One of the best experts on this subject based on the ideXlab platform.
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Agent models for self Motivated home assistant bots
2009 INTERNATIONAL CONFERNECE ON COMPUTATIONAL MODELS FOR LIFE SCIENCES (CMLS‐09), 2010Co-Authors: Kathryn E Merrick, Kamran ShafiAbstract:Modern society increasingly relies on technology to support everyday activities. In the past, this technology has focused on automation, using computer technology embedded in physical objects. More recently, there is an expectation that this technology will not just embed reactive automation, but also embed intelligent, proactive automation in the environment. That is, there is an emerging desire for novel technologies that can monitor, assist, inform or entertain when required, and not just when requested. This paper presents three self‐Motivated, home‐assistant bot applications using different self‐Motivated Agent models. Self‐Motivated Agents use a computational model of motivation to generate goals proactively. Technologies based on self‐Motivated Agents can thus respond autonomously and proactively to stimuli from their environment. Three prototypes of different self‐Motivated Agent models, using different computational models of motivation, are described to demonstrate these concepts.
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Agent Models for Self‐Motivated Home‐Assistant Bots
2010Co-Authors: Kathryn E Merrick, Kamran ShafiAbstract:Modern society increasingly relies on technology to support everyday activities. In the past, this technology has focused on automation, using computer technology embedded in physical objects. More recently, there is an expectation that this technology will not just embed reactive automation, but also embed intelligent, proactive automation in the environment. That is, there is an emerging desire for novel technologies that can monitor, assist, inform or entertain when required, and not just when requested. This paper presents three self‐Motivated, home‐assistant bot applications using different self‐Motivated Agent models. Self‐Motivated Agents use a computational model of motivation to generate goals proactively. Technologies based on self‐Motivated Agents can thus respond autonomously and proactively to stimuli from their environment. Three prototypes of different self‐Motivated Agent models, using different computational models of motivation, are described to demonstrate these concepts.
Sorabain Wolfheart De Lioncourt - One of the best experts on this subject based on the ideXlab platform.
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Multi-Agent-Systems and Applications - Motivated Agent Behaviour and Requirements Applied to Virtual Emergencies
Lecture Notes in Computer Science, 2020Co-Authors: Sorabain Wolfheart De Lioncourt, Michael LuckAbstract:Virtual environments provide a rich and varied domain for intelligent Agents, but questions of design and development in this context are still to be answered. An Agent with multiple requirements and limited or constrained resources must be able to make decisions as to how to divide those resources in order to satisfy its requirements. It may not be possible to satisfy all of them at once, so some may have to be sacrificed for the sake of those that are more important; in other cases a compromise may be possible in which all requirements are partially satisfied. This paper examines the kinds of requirements we may expect to have of an Agent in virtual environments, and describes how we can measure an Agent's preformance in this light. Such an analysis can be used as a conceptual design tool and as the basis of an Agent specification. An Agent architecture based on the BDI model is proposed in which design and implementation is decomposed in terms of requirements, and which allows the intuitive development of sophisticated Agents with multiple requirements in a dynamic virtual environment.
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Motivated Agent behaviour and requirements applied to virtual emergencies
Lecture Notes in Computer Science, 2002Co-Authors: Sorabain Wolfheart De Lioncourt, Michael LuckAbstract:Virtual environments provide a rich and varied domain for intelligent Agents, but questions of design and development in this context are still to be answered. An Agent with multiple requirements and limited or constrained resources must be able to make decisions as to how to divide those resources in order to satisfy its requirements. It may not be possible to satisfy all of them at once, so some may have to be sacrificed for the sake of those that are more important; in other cases a compromise may be possible in which all requirements are partially satisfied. This paper examines the kinds of requirements we may expect to have of an Agent in virtual environments, and describes how we can measure an Agent's preformance in this light. Such an analysis can be used as a conceptual design tool and as the basis of an Agent specification. An Agent architecture based on the BDI model is proposed in which design and implementation is decomposed in terms of requirements, and which allows the intuitive development of sophisticated Agents with multiple requirements in a dynamic virtual environment.