This pilot study evaluates the ability of machined learned algorithms to assist with the differential diagnosis of dementia subtypes based on brief (< 10 min) spontaneous speech samples. We analyzed 1 recordings of a brief spontaneous speech sample from 48 participants from 5 different groups: 4 types of dementia plus healthy controls. Recordings were analyzed using a speech recognition system optimized for speakerindependent spontaneous speech. Lexical and acoustic features were automatically extracted. The resulting feature profiles were used as input to a machine learning system that was trained to identify the diagnosis assigned to each research participant. Between groups lexical and acoustic differences features were detected in accordance with expectations from prior research literature suggesting that classifications were based on features consistent with human-observed symptomatology. Machine learning algorithms were able to identify participants' diagnostic group with accuracy comparable to existing diagnostic methods in use today. Results suggest this clinical speech analytic approach offers promise as an additional, objective and easily obtained source of diagnostic information for clinicians.
We report on our ongoing practical experience in designing, implementing, and deploying PTIME, a personalized agent for time management and meeting scheduling in an open, multi-agent environment. In developing PTIME as part of a larger assistive agent called CALO, we have faced numerous challenges, including usability, multi-agent coordination, scalable constraint reasoning, robust execution, and unobtrusive learning. Our research advances basic solutions to the fundamental problems; however, integrating PTIME into a deployed system has raised other important issues for the successful adoption of new technology. As a personal assistant, PTIME must integrate easily into a user's real environment, support her normal workflow, respect her authority and privacy, provide natural user interfaces, and handle the issues that arise with deploying such a system in an open environment.
The Disjunctive Temporal Problem with Uncertainty (DTPU) is an extension of the Disjunctive Temporal Problem (DTP) that accounts for events not under the control of the executing agent. We investigate the semantics of DTPU constraints, refining the existing notion that they are simply disjunctions of STPU constraints. We then develop the first sound and complete algorithm to determine whether Strong Controllability holds for a DTPU. We analyze the complexity of our algorithm with respect to the number of constraints in different classes, showing that, for several common subclasses of DTPUs, determining Strong Controllability has the same complexity as solving DTPs.
The emergence of web-based applications, such as electronic commerce and internet media, has coincided with growing recognition that many of the tasks people perform with computers can be performed better when the application adapts to its user. A popular example is Netflix, which gives customized movie recommendations to each user, using past movie ratings of the user and his or her friends. Netflix and other preference-aware interactive systems share the common aim of aiding the user in carrying out tasks-from finding a product to editing a document-by eliciting preferences from the user, inferring a preference model, and using the model to decide when and how to take action.The variety of preference-aware interactive applications includes recommender systems (such as Netflix or Amazon.com) that suggest items based on the user's similarity to other users or on previously viewed items, conversational systems that interact with the user in a simplified dialogue to perform a task, interfaces that adapt to the user's preferences and situation, and personal agents that can proactively support the user, modeling his or her needs and desires.In this article we review characteristic examples of intelligent, preference-aware interactive systems, survey the major questions, challenges and approaches with respect to preference representation, reasoning, and explanation and with respect to interactivity and task performed, and give an outlook on the potential of these systems. Throughout, we illustrate that designing a successful, adoptable system requires more than simply selecting the best form of representation, explanation, and reasoning. It requires a cohesive design that ensures the perceived benefits from using preferences significantly exceed the perceived costs of eliciting them.The interactivity of applications varies in nature, extent, modalities, and manifestation, as much as applications vary in their domains. Despite this diversity, the interactivity literature provides guidelines for designing an intelligent, interactive system (Horvitz 1999 Preferences for interactive applications concern not only the base task-the characteristics of what the user wants achieved with the application-but also the interactive process-the characteristics of how the user wants it achieved. Lieberman and Selker (2000) discuss the notion of the context of an interactive application. Preferences constitute one aspect of context. In situations and for applications where preferences are pertinent-most notably, the preferences of the system's user or users-failure to design and build the application around these preferences risks leaving the application out of context. Table 1 lists five aspects that collectively define a preference-aware interactive system. Associated with them is a set of technical challenges for the developer. We highlight four of these challenges that will be seen recurrently in the case studies to be discussed.First, the degree and nature of interaction must be tailored to the specific need and the context. Wou...
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