Wandering is a problematic behavior that is common among people with dementia (PwD), and is highly influenced by the elders’ background and by contextual factors specific to the situation. We have developed the Ambient Augmented Memory System (AAMS) to support the caregiver to implement interventions based on providing external memory aids to the PwD. To provide a successful intervention, it is required to use an individualized approach that considers the context of the PwD situation. To reach this end, we extended the AAMS system to include an ontological model to support the context-aware tailoring of interventions for wandering. In this paper, we illustrate the ontology flexibility to personalize the AAMS system to support direct and indirect interventions for wandering through mobile devices.

We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights. We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify algorithms accordingly.

Multiagent Resource Allocation (MARA) distributes a set of resources among a set of intelligent agents in order to respect the preferences of the agents and to maximize some measure of global utility, which may include minimizing total costs or maximizing total return. We are interested in MARA solutions that provide optimal or close-to-optimal allocation of resources in terms of maximizing a global welfare function with low communication and computation cost, with respect to the priority of agents, and temporal dependencies between resources. We propose an MDP approach for resource planning in multiagent environments. Our approach formulates internal preference modeling and success of each individual agent as a single MDP and then to optimize global utility, we apply a market-based solution to coordinate these decentralized MDPs.

In AI research, mechanism design is typically used to allocate tasks and resources to agents holding private information about their values for possible allocations. In this context, optimizing payments within the Groves class has recently received much attention, mostly under the assumption that agent's private information is single-dimensional. Our work tackles this problem in multi-parameter domains. Specifically, we develop a generic technique to look for a best Groves mechanism for any given mechanism design problem. Our method is based on partitioning the spaces of agent values and payment functions into regions, on each of which we are able to define a feasible linear payment function. Under certain geometric conditions on partitions of the two spaces this function is optimal. We illustrate our method by applying it to the problem of allocating heterogeneous items.

Multiagent resource allocation under uncertainty raises various computational challenges in terms of efficiency such as intractability, communication cost, and preference representation. To date most approaches do not provide efficient solutions for dynamic environments where temporal constraints pose particular challenges. We propose two techniques to cope with such settings: auctions to allocate fairly according to preferences, and MDPs to address stochasticity. This research seeks to determine the ideal combination between the two methods to handle wide range of allocation problems with reduced computation and communication cost between agents.

We consider an infinite mixture model of Gaussian processes that share mixture components between non-local clusters in data. Meeds and Osindero (2006) use a single Dirichlet process prior to specify a mixture of Gaussian processes using an infinite number of experts. In this paper, we extend this approach to allow for experts to be shared non-locally across the input domain. This is accomplished with a hierarchical double Dirichlet process prior, which builds upon a standard hierarchical Dirichlet process by incorporating local parameters that are unique to each cluster while sharing mixture components between them. We evaluate the model on simulated and real data, showing that sharing Gaussian process components non-locally can yield effective and useful models for richly clustered non-stationary, non-linear data.

We develop a novel absorption technique for large collections of factual assertions about individual objects. These assertions are commonly accompanied by implicit background knowledge and form a knowledge base. Both the assertions and the background knowledge are expressed in a suitable language of Description Logic and queries over such knowledge bases can be expressed as assertion retrieval queries. The proposed absorption technique significantly improves the performance of such queries, in particular in cases where a large number of object features are known for the objects represented in such a knowledge base. In addition to the absorption technique we present the results of a preliminary experimental evaluation that validates the efficacy of the proposed optimization.

We revisit a framework for designing peer-based intelligent tutoring systems motivated by McCalla's ecological approach, where learning is facilitated by the previous experiences of peers with a corpus of learning objects. Prior research demonstrated the value of a proposed algorithm for modeling student learning and for selecting the most beneficial learning objects to present to new students. In this paper, we first adjust the validation of this approach to demonstrate its ability to cope with errors in assessing the learning of student peers. We then deepen the representation of learning objects to reflect the expected time to completion and demonstrate how this may lead to more effective selection of learning objects for students, and thus more effective learning. As part of our exploration of these new adjustments, we offer insights into how the size of learning object repositories may affect student learning, suggesting future extensions for the model and its validation.

This paper tackles the problem of building situated prompting and assistance systems for guiding a human with a cognitive disability through a large domain containing multiple tasks. This problem is challenging because the target population has difficulty maintaining goals, recalling necessary steps and recognizing objects and potential actions (affordances), and therefore may not appear to be acting rationally. Prompts or cues from an automated system can be very helpful in this regard, but the domain is inherently partially observable due to sensor noise and uncertain human behaviours, making the task of selecting an appropriate prompt very challenging. Prior work has shown how such automated assistance for a single task can be modeled as a partially observable Markov decision process (POMDP). In this paper, we generalise this to multiple tasks, and show how to build a scalable, distributed and hierarchical controller. We demonstrate the algorithm in a set of simulated domains and show it can perform as well as the full model in many cases, and can give solutions to large problems (over 10 15 states and 10 9 observations) for which the full model fails to find a policy.

Considering few treatments are available to slow or stop neurodegenerative disorders, such as Alzheimer’s disease and related dementias (ADRD), modifying lifestyle factors to prevent disease onset are recommended. The Voice, Activity, and Location Monitoring system for Alzheimer’s disease (VALMA) is a novel ambulatory sensor system designed to capture natural behaviours across multiple domains to profile lifestyle risk factors related to ADRD. Objective measures of physical activity and sleep are provided by lower limb accelerometry. Audio and GPS location records provide verbal and mobility activity, respectively. Based on a familiar smartphone package, data collection with the system has proven to be feasible in community-dwelling older adults. Objective assessments of everyday activity will impact diagnosis of disease and design of exercise, sleep, and social interventions to prevent and/or slow disease progression.