Handling the misunderstanding in interactions: Definition and solution

Pham, Phuong; Rabah, Mourad; Estraillier, Pascal

doi:10.1037/e605252012-002

Cited by 3 publications

(5 citation statements)

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“…The context of our work is the decision making in Interactive Adaptive Systems (IAS). An IAS is a system that can adapt its execution according to users behaviours [1], [2]. The system checks users interactions and adapt application's scenario with respect to designer's constraints.…”

Section: Introductionmentioning

confidence: 99%

Trace-Based Weighting Approach for Multiple Criteria Decision Making

Ho¹,

Rabah²,

Nowakowski³

et al. 2014

JSW

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Decision-making has evolved as an interesting research problem for decision community. We consider a decision problem that takes into account several criteria called Multiple Criteria Decision Making (MCDM) in an interactive application for adaptive execution. In this paper, we present a method for automatically weighting criteria generation based on users' traces. In our method, we suggest a process that contains all steps describing alternately what is necessary to prepare in order to weight all criteria. We propose a modified method using Naïve Bayes network to exploit the traces (the past of users), there will be used as information for estimating the score of criteria. Experimental results are presented to illustrate a full process and an automatic generation of weighting criteria by a set of values.

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Section: Introductionmentioning

confidence: 99%

Trace-Based Weighting Approach for Multiple Criteria Decision Making

Ho¹,

Rabah²,

Nowakowski³

et al. 2014

JSW

Self Cite

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“…We give the following definition from our previous work (Pham et al, 2011): Two actors are in a misunderstanding state when (i) they are in interaction with each other, (ii) there are incoherent data in their local visions about the same fact, and (iii) these data are used during the interaction. A fact is considered as objective data or an absolute reference to the system's actors or resource states.…”

Section: Definition Of the Misunderstandingmentioning

confidence: 99%

“…This method is flexible, adaptive, and applicable in real time during application execution, but it may lead to an uncontrollable situation order or an infinite loop, if the post-conditions and pre-conditions do not contain sufficient data. To avoid this issue, we add a specific situation that handles the absence of post/pre-condition matching when necessary (Pham et al, 2011).…”

Section: 3mentioning

confidence: 99%

“…In our recent work (Pham et al, 2011;Trillaud et al, 2012), we have defined the misunderstanding in interaction as follows: When two or more system's actors have incoherent data in their local visions about the same fact f and these data are used during their interaction, this may cause an interaction deviation from the planned scenario. An actor may be a human user or a virtual system agent.…”

Section: Introductionmentioning

confidence: 99%

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A situation-based multi-agent architecture for handling misunderstandings in interactions

Pham

Rabah

Estraillier

2015

International Journal of Applied Mathematics and Computer Science

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During interactions, system actors may face up misunderstandings when their local states contain inconsistent data about the same fact. Misunderstandings in interactions are likely to reduce interactivity performances (deviation or deadlock) or even affect overall system behavior. In this paper, we characterize misunderstandings in interactions between system actors (that may be human users or system agents) in interactive adaptive systems. To deal with such misunderstandings and ensure state consistency, we present an agent-based architecture and a scenario structuring approach. The system includes several agents devoted to scenario unfolding, plot adaptation and consistency management. Scenario structuring is based on the notion of a situation that is an elementary building block dividing the interactions between systems' actors into contextual scenes. This pattern supports not only scenario execution but consistency management as well. In order to organize and control interactions, the situation contextualizes interactions and activity of the system's actors. It also includes prevention and tolerance agent-based mechanisms to deal with the misunderstandings and their causes. We validate our consistency management mechanisms using Uppaal simulation and provide some experimental results to show the effectiveness of our approach on an online distance learning case study.

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“…Each "situation" is a sequence of interactions between application's actors within a given shared context (Pham et al, 2012). Furthermore, the situation-based scenario uses interactive storytelling techniques as well as consistency management (Pham et al, 2011) in dynamical execution to ensure robustness and interactivity for online learning. It also facilitates the adaptability during the training session and improves the training quality by analyzing feedbacks and results.…”

Section: Introductionmentioning

confidence: 99%

Online Distant Learning Using Situation-Based Scenarios

Trillaud¹,

Pham²,

Rabah³

et al. 2012

Proceedings of the 4th International Conference on Computer Supported Education

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Abstract:This paper is an overview of our work to deal with scenario representation and management model in Online Distant Learning domain. Our approach is based on scenario structuring using the notion of "situation". A "situation" is an elementary building block dividing the interactions between actors into contextual scenes. This pattern not only supports the scenario execution, but also its creation and evolution. After discussing the global methodology to manage ODL applications and introducing the situation-based scenarios, we show how our methodology and situation structuring can be applied to and enhance ODL.

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Handling the misunderstanding in interactions: Definition and solution

Cited by 3 publications

References 1 publication

Trace-Based Weighting Approach for Multiple Criteria Decision Making

Trace-Based Weighting Approach for Multiple Criteria Decision Making

A situation-based multi-agent architecture for handling misunderstandings in interactions

Online Distant Learning Using Situation-Based Scenarios

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