Recently, geospatial abduction was introduced by the authors in [Shakarian et. al. 2010] as a way to infer unobserved geographic phenomena from a set of known observations and constraints between the two. In this paper, we introduce the SCARE-S2 software tool which applies geospatial abduction to the environment of Afghanistan. Unlike previous work, where we looked for small weapon caches supporting local attacks, here we look for insurgent high-value targets (HVT's), supporting insurgent operations in two provinces. These HVT's include the locations of insurgent leaders and major supply depots. Applying this method of inference to Afghanistan introduces several practical issues not addressed in previous work. Namely, we are conducting inference in a much larger area (24,940 sq km as compared to 675 sq km in previous work), on more varied terrain, and must consider the influence of many local tribes. We address all of these problems and evaluate our software on 6 months of real-world counter-insurgency data. We show that we are able to abduce regions of a relatively small area (on average, under 100 sq km and each containing, on average, 4.8 villages) that are more dense with HVT's (35 X more than the overall area considered).

This paper describes a system to help intelligence analysts track and analyze information being published in multiple sources, particularly open sources on the Web. The system integrates technology for Web harvesting, natural language extraction, and network analytics, and allows analysts to view and explore the results via a Web application. One of the difficult problems we address is the entity resolution problem, which occurs when there are multiple, differing ways to refer to the same entity. The problem is particularly complex when noisy data is being aggregated over time, there is no clean master list of entities, and the entities under investigation are intentionally being deceptive. Our system must not only perform entity resolution with noisy data, but must also gracefully recover when entity resolution mistakes are subsequently corrected. We present a case study in arms trafficking that illustrates the issues, and describe how they are addressed.

We report on an emerging application focused on the design of resilient long reach passive optical networks using combinatorial optimisation techniques. The objective of the application is to determine the optimal position and capacity of a set of metro nodes. We specifically consider dual parented networks whereby each customer must be associated with two metro nodes. An important property of such a placement is resilience to single node failure. Therefore excess capacity should be provided at each metro node in order to ensure that customers can be redistributed amongst the metro sites. Our application, as well as finding optimal node placements, can compute the minimum level of excess capacity on all metro nodes. In this paper we present three alternative approaches to optimal metro node placement.We present a detailed analysisof the impact of different placement approaches on the distribution of excess capacity throughout the network. We show that preferential distributions occur in practice, based on a case-study in Ireland. Finally we show that load and excess capacity provision are independent of each other.

Diabetes management is a difficult task for patients, who must monitor and control their blood glucose levels in order to avoid serious diabetic complications. It is a difficult task for physicians, who must manually interpret large volumes of blood glucose data to tailor therapy to the needs of each patient. This paper describes three emerging applications that employ AI to ease this task and shares difficulties encountered in transitioning AI technology from university researchers to patients and physicians.

Due to the rapid aging of the population, many technical solutions for the care of the elderly are being developed, often involving fall detection with accelerometers. We present a novel approach to fall detection with location sensors. In our application, a user wears up to four tags on the body whose locations are detected with radio sensors. This makes it possible to recognize the user’s activity, including falling any lying afterwards, and the context in terms of the location in the apartment. We compared fall detection using location sensors, accelerometers and accelerometers combined with the context. A scenario consisting of events difficult to recognize as falls or non-falls was used for the comparison. The accuracy of the methods that utilized the context was almost 40 percentage points higher compared to the methods without the context. The accuracy of pure location-based methods was around 10 percentage points higher than the accuracy of accelerometers combined with the context.

Our goal is to enable military planners to rapidly critique alternative battle plans by simulating multiple outcomes of adversarial plans. We describe a novel simulator, SimPath, that combines qualitative reasoning, a geographic information system (GIS), and targeted probabilistic calculations to envision how adversarial battle plans can play out. We outline the problem and describe the overall operation of the simulator. We then explain how qualitative process theory is extended with actions to model military tasks, how envisioning is factored to reduce combinatorial explosion, and how probabilities are computed for transitions and used to filter possibilities. Empirical results, including an experiment conducted by an independent evaluator, are summarized. The results show that it is possible to identify dozens of possible outcomes on each of 9 combinations of adversarial plans (COAs) in under two minutes. We close with a discussion of future work.

We propose an intelligent tutoring system that constructs a curriculum of hints and problems in order to teach a student skills with a rich dependency structure. We provide a template for building a multi-layered Dynamic Bayes Net to model this problem and describe how to learn the parameters of the model from data. Planning with the DBN then produces a teaching policy for the given domain. We test this end-to-end curriculum design system in two human-subject studies in the areas of finite field arithmetic and artificial language and show this method performs on par with hand-tuned expert policies.

The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates precise in text sentiment tagging as well as sentiment-oriented event summaries for a given stock, which are also aggregated into sentiment scores. Hence, TSS allows investors to get the essence of thousands of articles every day and may help them to make timely, informed trading decisions. The extracted sentiment is also shown to improve the accuracy of an existing document-level sentiment classifier.

In this paper, we investigate a novel application of online planning and scheduling:controlling an automated infeeder for a packaging line of foodand consumer packaged goods. In this system, products arrive continuously at high-speedfrom the end of the production line and need to be arranged into a specific configurationfor downstream primary and secondary packaging machines.In collaboration with a domain expert from the packaging industry,we developed an innovative design for a reconfigurable parallel infeed system usinga matrix of interchangeable smart belts. We also adapted our online model-basedPlantrol planner to this domain. Our planner can control various configurations ofthe new infeed system through simulation both in nominal planning and when runtimefailures occur. We are also building a small physical prototype to validate the newdesign and our software framework.

Redacting text documents has traditionally been a mostly manual activity, making it expensive and prone to disclosure risks. This paper describes a semi-automated system to ensure a specified level of privacy in text data sets. Recent work has attempted to quantify the likelihood of privacy breaches for text data. We build on these notions to provide a means of obstructing such breaches by framing it as a multi-class classification problem. Our system gives users fine-grained control over the level of privacy needed to obstruct sensitive concepts present in that data. Additionally, our system is designed to respect a user-defined utility metric on the data (such as disclosure of a particular concept), which our methods try to maximize while anonymizing. We describe our redaction framework, algorithms, as well as a prototype tool built in to Microsoft Word that allows enterprise users to redact documents before sharing them internally and obscure client specific information. In addition we show experimental evaluation using publicly available data sets that show the effectiveness of our approach against both automated attackers and human subjects.The results show that we are able to preserve the utility of a text corpus while reducing disclosure risk of the sensitive concept.