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Author

Anders Kofod-Petersen

Other affiliations: Telenor, SINTEF
Bio: Anders Kofod-Petersen is an academic researcher from Norwegian University of Science and Technology. The author has contributed to research in topics: Intelligent decision support system & Context (language use). The author has an hindex of 12, co-authored 55 publications receiving 683 citations. Previous affiliations of Anders Kofod-Petersen include Telenor & SINTEF.


Papers
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Journal ArticleDOI
01 Jun 2005
TL;DR: A multi-agent system for supplying context-sensitive services in a mobile environment and a two tier architecture implementing a general mechanism for aggregating and maintaining contextual information are proposed.
Abstract: Today the computer is changing from a big, grey, and noisy thing on our desk to a small, portable, and ever-networked item most of us are carrying around. This new found mobility imposes a shift in how we view computers and the way we work with them. When interaction can occur anywhere at any time it is imperative that the system adapts to the user in whatever situation the user is in. To facilitate this adaptivity we propose a two tier architecture. A middleware layer implementing a general mechanism for aggregating and maintaining contextual information. The second part offers automatic situation assessment through Case-Based Reasoning. We demonstrate a multi-agent system for supplying context-sensitive services in a mobile environment.

116 citations

Book ChapterDOI
TL;DR: This work demonstrates the first steps towards an ambient intelligent system, which is able to appreciate the environment and reason about occurring situations, through knowledge intensive case-based reasoning.
Abstract: Ambient Intelligence is a research area that has gained a lot of attention in recent years One of the most important issues for ambient intelligent systems is to perceive the environment and assess occurring situations, thus allowing systems to behave intelligently As the ambient intelligence area has been largely technology driven, the abilities of systems to understand their surroundings have largely been ignored This work demonstrates the first steps towards an ambient intelligent system, which is able to appreciate the environment and reason about occurring situations This situation awareness is achieved through knowledge intensive case-based reasoning

63 citations

Journal Article
TL;DR: In this article, the authors focus on the socio-technical aspects of artificial intelligence, namely how (specific types of) intelligent systems function in human workplace environments, and make use of theories from the fields of organisational psychology, sociology, and linguistics.
Abstract: This work focuses on the socio-technical aspects of artificial intelligence, namely how (specific types of) intelligent systems function in human workplace environments. The goal is first to get a better understanding of human needs and expectations when it comes to interaction with intelligent systems, and then to make use of the understanding gained in the process of designing and implementing such systems.The work presented focusses on a specific problem in developing intelligent systems, namely how the artefacts to be developed can fit smoothly into existing socio-cultural settings. To achieve this, we make use of theories from the fields of organisational psychology, sociology, and linguistics. This is in line with approaches commonly found in AI. However, most of the existing work deals with individual aspects, like how to mimic the behaviour or emulate methods of reasoning found in humans, whereas our work centers around the social aspect. Therefore, we base our work on theories that have not yet gained much attention in intelligent systems design. To be able to make them fruitful for intelligent systems research and development, we have to adapt them to the specific settings, and we have to transform them to suit the practical problems at hand.The specific theoretical frameworks we draw on are first and foremost activity theory and to a lesser degree semiotics. Activity theory builds on the works of Leont'ev. It is a descriptive tool to help understand the unity of consciousness and activity. Its focus lies on individual and collective work practise. One of its strengths, and the primary reason for its value in AI development, is the ability to identify the role of material artefacts in the work process. Halliday's systemic functional theory of language (SFL) is a social semiotic theory that sets out from the assumption that humans are social beings that are inclined to interact and that this interaction is inherently multimodal. We interact not just with each other, but with our own constructions and with our natural world. These are all different forms of interaction, but they are all sign processes.Due to the obvious time and spatial constraints, we cannot address all of the challenges that we face when building intelligent artefacts. In reducing the scope of the thesis, we have focused on the problem of explanation, and here in particular the problem of explanation from a user perspective. By putting social theories to work in the field of artificial intelligence, we show that results from other fields can be beneficial in understanding what explanatory capabilities are needed for a given intelligent system, and to ascertain in which situations an explanation should be delivered. Besides lessons learned in knowledge based system development, the most important input comes from activity theory.The second focus is the challenge of contextualisation. Here we show that work in other scientific fields can be put to use in the development of context aware or ambient intelligent systems. Again, we draw on results from activity theory and combine this with insights from semiotics.Explanations are themselves contextual, so the third challenge is to explore the space spanned by the two dimensions ability to explain and contextualisation. Again, activity theory is beneficial in resolving this issue.The different theoretical considerations have also led to some practical approaches. Working with activity theory helps to better understand what the relevant contextual aspects of a given application are and helps to develop models of context which are both grounded in the tradition of context aware systems design and are plausible from a cognitive point of view.Insights from an analysis of research in the knowledge based system area and activity theory have further lead to the amendment of a toolbox for requirements engineering, so called problem frames. New problem frames that target explanation aware ambient intelligent systems are presented. This is supplemented with work looking at the design of an actual system after the requirements have been elicited and specified. Thus, the socio-technical perspective on explanations is coupled with work that addresses knowledge representation issues, namely how to model sufficient knowledge to be able to deliver explanations.

59 citations

Book ChapterDOI
31 Jul 2005
TL;DR: In this paper, the authors propose the use of Activity Theory to first model context and further populate the model for assessing situations in a pervasive computing environment, through the socio-technical perspective given by Activity Theory, the knowledge intensive context model, utilised in ambient intelligent system, is designed.
Abstract: One of the cornerstones of any intelligent entity is the ability to understand how occurrences in the surrounding world influence its own behaviour. Different states, or situations, in its environment should be taken into account when reasoning or acting. When dealing with different situations, context is the key element used to infer possible actions and information needs. The activities of the perceiving agent and other entities are arguably one of the most important features of a situation; this is equally true whether the agent is artificial or not. This work proposes the use of Activity Theory to first model context and further on populate the model for assessing situations in a pervasive computing environment. Through the socio-technical perspective given by Activity Theory, the knowledge intensive context model, utilised in our ambient intelligent system, is designed.

49 citations

Proceedings Article
01 Jan 2006
TL;DR: By putting social theories to work in the field of artificial intelligence, this work shows that results from other fields can be beneficial in understanding what explanatory capabilities are needed for a given intelligent system, and to ascertain in which situations an explanation should be delivered.
Abstract: This work focuses on the socio-technical aspects of artificial intelligence, namely how (specific types of) intelligent systems function in human workplace environments. The goal is first to get a better understanding of human needs and expectations when it comes to interaction with intelligent systems, and then to make use of the understanding gained in the process of designing and implementing such systems.The work presented focusses on a specific problem in developing intelligent systems, namely how the artefacts to be developed can fit smoothly into existing socio-cultural settings. To achieve this, we make use of theories from the fields of organisational psychology, sociology, and linguistics. This is in line with approaches commonly found in AI. However, most of the existing work deals with individual aspects, like how to mimic the behaviour or emulate methods of reasoning found in humans, whereas our work centers around the social aspect. Therefore, we base our work on theories that have not yet gained much attention in intelligent systems design. To be able to make them fruitful for intelligent systems research and development, we have to adapt them to the specific settings, and we have to transform them to suit the practical problems at hand.The specific theoretical frameworks we draw on are first and foremost activity theory and to a lesser degree semiotics. Activity theory builds on the works of Leont'ev. It is a descriptive tool to help understand the unity of consciousness and activity. Its focus lies on individual and collective work practise. One of its strengths, and the primary reason for its value in AI development, is the ability to identify the role of material artefacts in the work process. Halliday's systemic functional theory of language (SFL) is a social semiotic theory that sets out from the assumption that humans are social beings that are inclined to interact and that this interaction is inherently multimodal. We interact not just with each other, but with our own constructions and with our natural world. These are all different forms of interaction, but they are all sign processes.Due to the obvious time and spatial constraints, we cannot address all of the challenges that we face when building intelligent artefacts. In reducing the scope of the thesis, we have focused on the problem of explanation, and here in particular the problem of explanation from a user perspective. By putting social theories to work in the field of artificial intelligence, we show that results from other fields can be beneficial in understanding what explanatory capabilities are needed for a given intelligent system, and to ascertain in which situations an explanation should be delivered. Besides lessons learned in knowledge based system development, the most important input comes from activity theory.The second focus is the challenge of contextualisation. Here we show that work in other scientific fields can be put to use in the development of context aware or ambient intelligent systems. Again, we draw on results from activity theory and combine this with insights from semiotics.Explanations are themselves contextual, so the third challenge is to explore the space spanned by the two dimensions ability to explain and contextualisation. Again, activity theory is beneficial in resolving this issue.The different theoretical considerations have also led to some practical approaches. Working with activity theory helps to better understand what the relevant contextual aspects of a given application are and helps to develop models of context which are both grounded in the tradition of context aware systems design and are plausible from a cognitive point of view.Insights from an analysis of research in the knowledge based system area and activity theory have further lead to the amendment of a toolbox for requirements engineering, so called problem frames. New problem frames that target explanation aware ambient intelligent systems are presented. This is supplemented with work looking at the design of an actual system after the requirements have been elicited and specified. Thus, the socio-technical perspective on explanations is coupled with work that addresses knowledge representation issues, namely how to model sufficient knowledge to be able to deliver explanations.

49 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

01 Jan 1964
TL;DR: In this paper, the notion of a collective unconscious was introduced as a theory of remembering in social psychology, and a study of remembering as a study in Social Psychology was carried out.
Abstract: Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

5,690 citations

01 Jan 2012

3,692 citations

Journal ArticleDOI
TL;DR: Polanyi is at pains to expunge what he believes to be the false notion contained in the contemporary view of science which treats it as an object and basically impersonal discipline.
Abstract: The Study of Man. By Michael Polanyi. Price, $1.75. Pp. 102. University of Chicago Press, 5750 Ellis Ave., Chicago 37, 1959. One subtitle to Polanyi's challenging and fascinating book might be The Evolution and Natural History of Error , for Polanyi is at pains to expunge what he believes to be the false notion contained in the contemporary view of science which treats it as an object and basically impersonal discipline. According to Polanyi not only is this a radical and important error, but it is harmful to the objectives of science itself. Another subtitle could be Farewell to Detachment , for in place of cold objectivity he develops the idea that science is necessarily intensely personal. It is a human endeavor and human point of view which cannot be divorced from nor uprooted out of the human matrix from which it arises and in which it works. For a good while

2,248 citations