115 research outputs found
Building cost-benefit models of information interactions
Modeling how people interact with search interfaces has been of particular interest and importance to the field of Interactive Information Retrieval. Recently, there has been a move to developing formal models of the interaction between the user and the system, whether it be to: (i) run a simulation, (ii) conduct an economic analysis, (iii) measure system performance, or (iv) simply to better understand user interactions and hypothesise about user behaviours. In such models, they consider the costs and the benefits that arise through the interaction with the interface/system and the information surfaced during the course of interaction. In this half day tutorial, we will focus on describing a series of cost-benefit models that have been proposed in the literature and how they have been applied in various scenarios. The tutorial will be structured into two parts. First, we will provide an overview of Decision Theory and Cost-Benefit Analysis techniques, and how they can and have be applied to a variety of Interactive Information Retrieval scenarios. For example, when do facets helps?, under what conditions are query suggestions useful? and is it better to bookmark or re-find? The second part of the tutorial will be dedicated to building cost-benefit models where we will discuss different techniques to build and develop such models. In the practical session, we will also discuss how costs and benefits can be estimated, and how the models can help inform and guide experimentation. During the tutorial participants will be challenged to build cost models for a number of problems (or even bring their own problems to solve)
Intelligent e-Learning Systems: An Educational Paradigm Shift
Learning is the long process of transforming information as well as experience into knowledge, skills, attitude and behaviors. To make up the wide gap between the demand of increasing higher education and comparatively limited resources, more and more educational institutes are looking into instructional technology. Use of online resources not only reduces the cost of education but also meet the needs of society. Intelligent e-learning has become one of the important channels to reach out to students exceeding geographic boundaries. Besides this, the characteristics of e-learning have complicated the process of education, and have brought challenges to both instructors and students. This paper will focus on the discussion of different discipline of intelligent e-learning like scaffolding based e-learning, personalized e-learning, confidence based e-learning, intelligent tutoring system, etc. to illuminate the educational paradigm shift in intelligent e-learning system
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications
The rapid development in the field of Large Language Models (LLMs) has led to
a surge in applications that facilitate collaboration among multiple agents to
assist humans in their daily tasks. However, a significant gap remains in
assessing whether LLM-powered applications genuinely enhance user experience
and task execution efficiency. This highlights the pressing need for methods to
verify utility of LLM-powered applications, particularly by ensuring alignment
between the application's functionality and end-user needs. We introduce
AgentEval provides an implementation for the math problems, a novel framework
designed to simplify the utility verification process by automatically
proposing a set of criteria tailored to the unique purpose of any given
application. This allows for a comprehensive assessment, quantifying the
utility of an application against the suggested criteria. We present a
comprehensive analysis of the robustness of quantifier's work
Towards a complete multiple-mechanism account of predictive language processing [Commentary on Pickering & Garrod]
Although we agree with Pickering & Garrod (P&G) that prediction-by-simulation and prediction-by-association are important mechanisms of anticipatory language processing, this commentary suggests that they: (1) overlook other potential mechanisms that might underlie prediction in language processing, (2) overestimate the importance of prediction-by-association in early childhood, and (3) underestimate the complexity and significance of several factors that might mediate prediction during language processing
Modelling interaction with economic models of search
Understanding how people interact when searching is central to the study of Interactive Information Retrieval (IIR). Most of the prior work has either been conceptual, observational or empirical. While this has led to numerous insights and findings regarding the interaction between users and systems, the theory has lagged behind. In this paper, we extend the recently proposed search economic theory to make the model more realistic. We then derive eight interaction based hypotheses regarding search behaviour. To validate the model, we explore whether the search behaviour of thirty-six participants from a lab based study is consistent with the theory. Our analysis shows that observed search behaviours are in line with predicted search behaviours and that it is possible to provide credible explanations for such behaviours. This work describes a concise and compact representation of search behaviour providing a strong theoretical basis for future IIR research
Using contextual information to understand searching and browsing behavior
There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications
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