Antti Kangasrääsiö

Improving Controllability and Predictability of Interactive Recommendation Interfaces for Exploratory Search

In exploratory search, when a user directs a search engine using uncertain relevance feedback, usability problems regarding controllability and predictability may arise. One problem is that the user is often modelled as a passive source of relevance information, instead of an active entity trying to steer the system based on evolving information needs. This may cause the user to feel that the response of the system is inconsistent with her steering. Another problem arises due to the sheer size and complexity of the information space, and hence of the system, as it may be difficult for the user to anticipate the consequences of her actions in this complex environment. These problems can be mitigated by interpreting the users actions as setting a goal for an optimization problem regarding the system state, instead of passive relevance feedback, and by allowing the user to see the predicted effects of an action before committing to it. In this paper, we present an implementation of these improvements in a visual user-controllable search interface. A user study involving exploratory search for scientific literature gives some indication on improvements in task performance, usability, perceived usefulness and user acceptance.

Agent-based modeling and simulation of a smart grid: A case study of communication effects on frequency control

Abstract A smart grid is the next generation power grid focused on providing increased reliability and efficiency in the wake of integration of volatile distributed energy resources. For the development of the smart grid, the modeling and simulation infrastructure is an important concern. This study presents an agent-based model for simulating different smart grid frequency control schemes, such as demand response. The model can be used for combined simulation of electrical, communication and control dynamics. The model structure is presented in detail, and the applicability of the model is evaluated with four distinct simulation case examples. The study confirms that an agent-based modeling and simulation approach is suitable for modeling frequency control in the smart grid. Additionally, the simulations indicate that demand response could be a viable alternative for providing primary control capabilities to the smart grid, even when faced with communication constraints.

Inferring Cognitive Models from Data using Approximate Bayesian Computation

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (ABC) to condition model parameters to data and prior knowledge. As the case study we examine menu interaction, where we have click time data only to infer a cognitive model that implements a search behaviour with parameters such as fixation duration and recall probability. Our results demonstrate that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. ABC provides ample opportunities for theoretical HCI research by allowing principled inference of model parameter values and their uncertainty.

Interactive Modeling of Concept Drift and Errors in Relevance Feedback

In exploratory search tasks, users usually start with considerable uncertainty about their search goals, and so the search intent of the user may be volatile as the user is constantly learning and reformulating her search hypothesis during the search. This may lead to a noticeable concept drift in the relevance feedback given by the user. We formulate a Bayesian regression model for predicting the accuracy of each individual user feedback and thus find outliers in the feedback data set. To accompany this model, we introduce a timeline interface that visualizes the feedback history to the user and gives her suggestions on which past feedback is likely in need of adjustment. This interface also allows the user to adjust the feedback accuracy inferences made by the model. Simulation experiments demonstrate that the performance of the new user model outperforms a simpler baseline and that the performance approaches that of an oracle, given a small amount of additional user interaction. A user study shows that the proposed modeling technique, combined with the timeline interface, made it easier for the users to notice and correct mistakes in their feedback, resulted in better and more diverse recommendations, allowed users to easier find items they liked, and was more understandable.

Dealing with Concept Drift in Exploratory Search: An Interactive Bayesian Approach

In exploratory search, when the user formulates a query iteratively through relevance feedback, it is likely that the feedback given earlier requires adjustment later on. The main reason for this is that the user learns while searching, which causes changes in the relevance of items and features as estimated by the user -- a phenomenon known as {it concept drift}. It might be helpful for the user to see the recent history of her feedback and get suggestions from the system about the accuracy of that feedback. In this paper we present a timeline interface that visualizes the feedback history, and a Bayesian regression model that can estimate jointly the users current interests and the accuracy of each user feedback. We demonstrate that the user model can improve retrieval performance over a baseline model that does not estimate accuracy of user feedback. Furthermore, we show that the new interface provides usability improvements, which leads to the users interacting more with it.

Inverse reinforcement learning from summary data

Inverse reinforcement learning (IRL) aims to explain observed strategic behavior by fitting reinforcement learning models to behavioral data. However, traditional IRL methods are only applicable when the observations are in the form of state-action paths. This assumption may not hold in many real-world modeling settings, where only partial or summarized observations are available. In general, we may assume that there is a summarizing function

Inverse Modeling of Complex Interactive Behavior with ABC.

\sigma

Feasibility of Agent-Based Modeling and Simulation in Modeling Waste Value Chains

σ, which acts as a filter between us and the true state-action paths that constitute the demonstration. Some initial approaches to extending IRL to such situations have been presented, but with very specific assumptions about the structure of