Our work has clearly revealed the roots of qualitative reasoning in AI in its attempts to formali... more Our work has clearly revealed the roots of qualitative reasoning in AI in its attempts to formalize and model common-sense physical knowledge as well as human reasoning mechanisms. Since AI — as stated by [Newell 90] — provides a theoretical infrastructure for the study of human cognition, we may also conclude that qualitative reasoning aims at establishing a cognitive theory of “non-numerical” process description and at automating the phase of model building. And AI still constitutes the main background of qualitative reasoning. However, since qualitative reasoning deals with physical systems and their changes in time, basic concepts about dynamic systems such as state diagrams, trajectories, state variables or input — output relations have been introduced. Thus, simulation and system theory constitute a second basis of this approach. This does not seem to be surprising, as they both deal with the modeling of dynamic systems and the generation of their behavior. Nevertheless, although there exists an evident similarity between the semantics of both areas, the structural descriptions as well as the behavior generation mechanisms are derived from AI, based on mathematical concepts. And, as already stated, we can identify cognitive science as a further area close to qualitative reasoning. This is also shown by the discussion about the problem of causality. Because of shortcomings of the early developments — mainly the problem of ambiguity — further knowledge in the form of quantitative information and more elaborated reasoning techniques was integrated. However, these improvements were mainly based on well-known concepts of fields outside AI, as for example the non-crossing rules of trajectories or Markov chains, which we have introduced in this paper. Thus, we can identify qualitative reasoning as an interdisciplinary approach.
@inproceedings{TUW-140125, author = {Ricci, Francesco and Werthner, Hannes}, title = {Case based ... more @inproceedings{TUW-140125, author = {Ricci, Francesco and Werthner, Hannes}, title = {Case based destination recommendation over an XML data repository}, booktitle = {Proceedings of ENTER Conference 2001}, year = {2001}, publisher = {Springer Verlag}, note = {Vortrag: ENTER Conference 2001, Montreal, Canada; 2001-00-00} } Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.
A behavioural framework for the development of destination recommendation systems (DRSs) is propo... more A behavioural framework for the development of destination recommendation systems (DRSs) is proposed that takes into account the specific characteristics of travel information search and decision making. Specifically, it outlines several design guidelines for destination recommendation systems that follow from the discussion of the various behavioural components. It is concluded that the success of a specific DRS will largely depend on its ability to anticipate and creatively respond to transformations in the personal and situational needs of its users.
Electronic Commerce and Web Technologies, Oct 1, 2006
... Yongjian Fu, Cleveland State University, USA Stephane Gagnon, New Jersey Institute of ... Vic... more ... Yongjian Fu, Cleveland State University, USA Stephane Gagnon, New Jersey Institute of ... Victoria Torres, Valencia University of Technology, Spain Pedro Valderas, Valencia University of ... 92 Gustavo Rossi, Andres Nieto, Luciano Mengoni, Liliana Nuno Silva Mobile Commerce ...
Interface metaphors are credited with the capability of facilitating interface usability and lear... more Interface metaphors are credited with the capability of facilitating interface usability and learnability from the human-computer interaction (HCI) perspective. On travel-related websites, they can help travellers plan their trips and make the trip-planning process more entertaining and engaging. This chapter conceptualizes interface metaphors on travel-related websites by examining the functional roles they play. Implications for research on interface metaphors and travellers' trip-planning experience are also discussed.
This part describes the basic approaches used in qualitative reasoning as well as the underlying ... more This part describes the basic approaches used in qualitative reasoning as well as the underlying qualitative calculus and the different dimensions of mapping a quantitative model onto a qualitative one. Additionally, we provide a first classification of these methods. We separate the description of the modeling perspectives of the respective approaches in the next chapter from the detailed discussion of their specific reasoning techniques in chapter II.4 in order to underline the basic principles and their differences. However, also short descriptions of the reasoning mechanisms are provided in the first chapter. We use the following notation: if x denotes a variable or quantity in a numerical model, [x] or x denotes its qualitative equivalent. The same is true for operations: addition becomes ⨁, multiplication ⨂ and so on.6
In this paper we analyze descriptions of group decision-making processes provided by 200 individu... more In this paper we analyze descriptions of group decision-making processes provided by 200 individuals organized in 55 groups. The goal of the paper is to show how such an analysis can be used when designing more efficient group recommender systems. To this end, we demonstrate that a) the provided group decision-making process descriptions can be systematically characterized by certain qualitatively identified aspects, b) the decision-making process that is adopted by a specific group is related to the characteristics of individuals in that group as well as to the characteristics of the group as a whole, and c) the decision reaching approach that a group naturally adopted can be determined, to a considerable extent, by certain characteristics of the group. Therefore, by examining real groups in a natural scenario, we can learn how a group recommender system should adapt when supporting group decision-making processes.
Our work has clearly revealed the roots of qualitative reasoning in AI in its attempts to formali... more Our work has clearly revealed the roots of qualitative reasoning in AI in its attempts to formalize and model common-sense physical knowledge as well as human reasoning mechanisms. Since AI — as stated by [Newell 90] — provides a theoretical infrastructure for the study of human cognition, we may also conclude that qualitative reasoning aims at establishing a cognitive theory of “non-numerical” process description and at automating the phase of model building. And AI still constitutes the main background of qualitative reasoning. However, since qualitative reasoning deals with physical systems and their changes in time, basic concepts about dynamic systems such as state diagrams, trajectories, state variables or input — output relations have been introduced. Thus, simulation and system theory constitute a second basis of this approach. This does not seem to be surprising, as they both deal with the modeling of dynamic systems and the generation of their behavior. Nevertheless, although there exists an evident similarity between the semantics of both areas, the structural descriptions as well as the behavior generation mechanisms are derived from AI, based on mathematical concepts. And, as already stated, we can identify cognitive science as a further area close to qualitative reasoning. This is also shown by the discussion about the problem of causality. Because of shortcomings of the early developments — mainly the problem of ambiguity — further knowledge in the form of quantitative information and more elaborated reasoning techniques was integrated. However, these improvements were mainly based on well-known concepts of fields outside AI, as for example the non-crossing rules of trajectories or Markov chains, which we have introduced in this paper. Thus, we can identify qualitative reasoning as an interdisciplinary approach.
@inproceedings{TUW-140125, author = {Ricci, Francesco and Werthner, Hannes}, title = {Case based ... more @inproceedings{TUW-140125, author = {Ricci, Francesco and Werthner, Hannes}, title = {Case based destination recommendation over an XML data repository}, booktitle = {Proceedings of ENTER Conference 2001}, year = {2001}, publisher = {Springer Verlag}, note = {Vortrag: ENTER Conference 2001, Montreal, Canada; 2001-00-00} } Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.
A behavioural framework for the development of destination recommendation systems (DRSs) is propo... more A behavioural framework for the development of destination recommendation systems (DRSs) is proposed that takes into account the specific characteristics of travel information search and decision making. Specifically, it outlines several design guidelines for destination recommendation systems that follow from the discussion of the various behavioural components. It is concluded that the success of a specific DRS will largely depend on its ability to anticipate and creatively respond to transformations in the personal and situational needs of its users.
Electronic Commerce and Web Technologies, Oct 1, 2006
... Yongjian Fu, Cleveland State University, USA Stephane Gagnon, New Jersey Institute of ... Vic... more ... Yongjian Fu, Cleveland State University, USA Stephane Gagnon, New Jersey Institute of ... Victoria Torres, Valencia University of Technology, Spain Pedro Valderas, Valencia University of ... 92 Gustavo Rossi, Andres Nieto, Luciano Mengoni, Liliana Nuno Silva Mobile Commerce ...
Interface metaphors are credited with the capability of facilitating interface usability and lear... more Interface metaphors are credited with the capability of facilitating interface usability and learnability from the human-computer interaction (HCI) perspective. On travel-related websites, they can help travellers plan their trips and make the trip-planning process more entertaining and engaging. This chapter conceptualizes interface metaphors on travel-related websites by examining the functional roles they play. Implications for research on interface metaphors and travellers' trip-planning experience are also discussed.
This part describes the basic approaches used in qualitative reasoning as well as the underlying ... more This part describes the basic approaches used in qualitative reasoning as well as the underlying qualitative calculus and the different dimensions of mapping a quantitative model onto a qualitative one. Additionally, we provide a first classification of these methods. We separate the description of the modeling perspectives of the respective approaches in the next chapter from the detailed discussion of their specific reasoning techniques in chapter II.4 in order to underline the basic principles and their differences. However, also short descriptions of the reasoning mechanisms are provided in the first chapter. We use the following notation: if x denotes a variable or quantity in a numerical model, [x] or x denotes its qualitative equivalent. The same is true for operations: addition becomes ⨁, multiplication ⨂ and so on.6
In this paper we analyze descriptions of group decision-making processes provided by 200 individu... more In this paper we analyze descriptions of group decision-making processes provided by 200 individuals organized in 55 groups. The goal of the paper is to show how such an analysis can be used when designing more efficient group recommender systems. To this end, we demonstrate that a) the provided group decision-making process descriptions can be systematically characterized by certain qualitatively identified aspects, b) the decision-making process that is adopted by a specific group is related to the characteristics of individuals in that group as well as to the characteristics of the group as a whole, and c) the decision reaching approach that a group naturally adopted can be determined, to a considerable extent, by certain characteristics of the group. Therefore, by examining real groups in a natural scenario, we can learn how a group recommender system should adapt when supporting group decision-making processes.
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Papers by Hannes Werthner