Personal communication and relationships within spatially distributed or separated groups can be difficult to establish and maintain. A promising approach investigated with respect to this problem are ambient intelligence and smart environments equipped with perception and communication technology. These technologies require a standardized way to access sensors, actuators, and to develop applications for them to be usable. Furthermore, they have to address concerns like privacy in order to be accepted. We propose a middleware based on a distributed reasoning concept and a qualitative spatial privacy aware representation to address these requirements.

In spatio-temporal reasoning, granularity is one of the factors to be considered when aiming at an effective and efficient representation of space and time. There is a large body of work which addresses the issue of granularity by representing space and time on a qualitative level. Other approaches use a predefined scale which implicitly determines granularity (e.g., seconds, minutes, hours, days, month, etc.). However, there are situations where the right level of granularity is unknown in the beginning, and is only determined in the problem solving process itself. This is the case in machine learning, where the learner has to find a representation for a problem and with that the right granularity for representing space and time. This paper introduces an algorithm which determines the most appropriate level of granularity during training. It uses several description logic learners as the learners, and the positive and negative examples presented to them as the determinators for refining coarse temporal representations to the most appropriate level of granularity.

The cognitive interpretation of perceptual data (e.g., from video, depth, motion sensors) requires the representational and inferential mediation of commonsense and qualitative abstractions of space, actions, events, change, and interaction. General methods and benchmarks for high-level cognitive interpretation, and their seamless integration and access within large-scale projects concerned with cognitive vision, robotics, hybrid-intelligent systems are necessary. We present the Rotunde initiative as a particular instance of a challenging smart meeting cinematography concept primarily concerning human activity interpretation. The Rotunde initiative aims to release general tools (e.g., for reasoning and control), methodological and performance benchmarks, and developmental aids (e.g., management and visualisation of complex spatio-temporal data) for the cognitive interpretation of interaction.

In exploratory domains, agents' actions map onto logs of behavior that include switching between activities, extraneous actions, and mistakes. These aspects create a challenging plan recognition problem. This paper presents a new algorithm for inferring students' activities in exploratory domains that is evaluated empirically using a new type of flexible and open-ended educational software for science education. Such software has been shown to provide a rich educational environment for students, but challenge teachers to keep track of students' progress and to assess their performance. The algorithm decomposes students’ complete interaction histories to create hierarchies of interdependent tasks that describe their activities using the software. It matches students' actions to a predefined grammar in a way that reflects that students solve problems in a modular fashion but may still interleave between their activities. The algorithm was empirically evaluated on people’s interaction with two separate software systems for simulating a chemistry laboratory and for statistics education. It was separately compared to the state-of-the-art recognition algorithms for each of the software. The results show that the algorithm was able to correctly infer students' activities significantly more often than the state-of-the-art, and was able to generalize to both of the software systems with no intervention.

In many applications a user attempts to configure a complex object with many possible internal choices. Recommendation engines that automatically configure such objects given user preferences and constraints, may provide much value in such cases. These applications offer the user various methods to provide the input and generate appropriate recommendations. It is likely, though, that the user will not be able to fully express her preferences and constraints, requiring a phase of manual tuning of the recommended configuration. We suggest that following this manual revision, additional constraints and preferences can be automatically collected, and the recommended configuration can be automatically improved. Specifically, we suggest a recommender component that takes as input an initial manual configuration of a complex object, deduces certain user preferences and constraints from this configuration, and constructs an alternative configuration. We show an appealing application for our method in complex trip planning, and demonstrate its usability in a user study.

The recommendation of additional shopping items that are potentially interesting for the customer has become a standard feature of modern online stores. In academia, research on recommender systems (RS) is mostly centered around approaches that rely on explicit item ratings and long-term user profiles. In practical environments, however, such rating information is often very sparse and for a large fraction of the users very little is known about their preferences. Furthermore, in particular when the shop offers products from a variety of categories, the decision of what should be recommended can strongly depend on the user's current short-term interests and the navigational context. In this paper, we report the results of an initial experimental analysis evaluating the predictive accuracy of different contextualized and non-contextualized recommendation strategies and discuss the question of appropriate experimental designs for such types of evaluations. To that purpose, we introduce a parameterizable protocol that supports session-specific accuracy measurements. Our analysis, which was based on log data obtained from a large online retailer for clothing and lifestyle products, shows that even a comparably simple contextual post-processing approach based on product features can leverage short-term user interests to increase the accuracy of the recommendations.

We consider the problem of personalized text-based music retrieval where users' history of preferences are taken into account in addition to their issued textual queries.Current retrieval methods mostly rely on songs meta-data. This limits the query vocabulary. Moreover, it is very costly to gather this information in large collections of music. Alternatively, we use music annotations retrieved from social tagging Websites such as last.fm and use them as textual descriptions of songs. Considering a user's profile and using preference patterns of music among all users, as in collaborative filtering approaches, can be useful in providing personalized and more satisfactory results. The main challenge is how to include both users' profiles and the songs meta-data in the retrieval model. In this paper, we propose a hierarchical probabilistic model that takes into account the users' preference history as well as tag co-occurrences in songs. Our model is an extension of LDA where topics are formed as joint clusterings of songs and tags. These topics capture the tag associations and user preferences and correspond to different music tastes. Each user's profile is represented as a distribution over topics which shows the user's interests in different types of music.We will explain how our model can be used for contextual retrieval. Our experimental results show significant improvement in retrieval when user profiles are taken into account.

We have deployed a fleet of robots that pickup and deliver items requested by users in an office building. Users specify time windows in which the items should be picked up and delivered, and send in requests online. Our goal is to form a schedule which picks up and delivers the items as quickly as possible at the lowest cost. We introduce an auction-based scheduling algorithm which plans to transfer items between robots to make deliveries more efficiently. The algorithm can obey either hard or soft time constraints. We discuss how to replan in response to newly requested items, cancelled requests, delayed robots, and robot failures. We demonstrate the effectiveness of our approach through execution on robots, and examine the effect of transfers on large simulated problems.

Detection of cognitive impairment, especially at the early stages, is critical. Such detection has traditionally been performed manually by one or more clinicians based on reports and test results. Machine learning algorithms offer an alternative method of detection that may provide an automated process and valuable insights into diagnosis and classification. In this paper, we explore the use of neuropsychological and demographic data to predict Clinical Dementia Rating (CDR) scores (no dementia, very mild dementia, dementia) and clinical diagnoses (cognitively healthy, mild cognitive impairment, dementia) through the implementation of four machine learning algorithms, naïve Bayes (NB), C4.5 decision tree (DT), back-propagation neural network (NN), and support vector machine (SVM). Additionally, a feature selection method for reducing the number of neuropsychological and demographic data needed to make an accurate diagnosis was investigated. The NB classifier provided the best accuracies, while the SVM classifier proved to offer some of the lowest accuracies. We also illustrate that with the use of feature selection, accuracies can be improved. The experiments reported in this paper indicate that artificial intelligence techniques can be used to automate aspects of clinical diagnosis of individuals with cognitive impairment.

Network medicine is an emerging paradigm for studying the co-occurrence between diseases. While diseases are often interlinked through complex patterns, most of the existing work in this area has focused on studying pairwise relationships between diseases. In this paper, we use a state-of-the-art Markov network learning method to learn interactions between musculoskeletal disorders and cardiovascular diseases and compare this to pairwise approaches. Our experimental results confirm that the sophisticated structure learner produces more accurate models, which can help reveal interesting patterns in the co-occurrence of diseases.