A cognitive robot may face failures during the execution of its actions in the physical world. In this paper, we investigate how robots can ensure robustness by gaining experience on action executions, and we propose a lifelong experimental learning method. We use Inductive Logic Programming (ILP) as the learning method to frame new hypotheses. ILP provides first-order logic representations of the derived hypotheses that are useful for reasoning and planning processes. Furthermore, it can use background knowledge to represent more advanced rules. Partially specified world states can also be easily represented in these rules. All these advantages of ILP make this approach superior to attribute-based learning approaches. Experience gained through incremental learning is used as a guide to future decisions of the robot for robust execution. The results on our Pioneer 3DX robot reveal that the hypotheses framed for failure cases are sound and ensure safety in future tasks of the robot.

Robotic plan execution has traditionally assumed that goals are articulated prior to mission execution. As robots have become persistent and increasingly moved into real-world environments, this assumption is not necessarily true; for instance a user can decide to give a new objective to the robot for inclusion in the plan being formulated, add newer goals, or modify others queued for execution. In most systems this leads, at best, to a suboptimal final plan or possibly to the exclusion of objectives, either of which could have been avoided, should the robot have executed its initial plan differently. We first articulate and then demonstrate a preliminary approach to this problem motivated by a marine robotics domain. We do so with an execution policy that is sufficient to disambiguate actions for execution within a flexible temporal continuous plan execution system. The resulting algorithmic complexity is linear in the number of actions and causal links of an existing partial plan.

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.

We present a technique to leverage annotator rationale an- notations for ventilator assisted pneumonia (VAP) classifi- cation. Given an annotated training corpus of 1344 narrative chest X-ray reports, we report results for two supervised classification tasks: Critical Pulmonary Infection Score (CPIS) and the likelihood of Pneumonia (PNA). For both tasks, our training data contain annotator rationale snippets (i.e., spans of text that are relevant to annotator decisions). Because we assume that the snippet is not marked in the test data, we first built a sequential labeler to detect the location of snippets. The detected snippets are then used by the CPIS and PNA classifiers. Our experiments demonstrate that having access to detected annotator rationale leads to an incremental improvement in classification accuracy from 0.858 to 0.871 for CPIS, and from 0.785 to 0.821 for PNA.

There is a pressing need in clinical practice to mitigate (identify and address) adverse interactions that occur when a comorbid patient is managed according to multiple concurrently applied disease-specific clinical practice guidelines (CPGs). We describe an automatic algorithm for mitigating undesirable interactions for pairs of CPGs. The algorithm constructs logical models of processed CPGs and employs constraint logic programming to solve them. It handles two important issues frequently occurring in CPGs - iterative actions forming a cycle and numerical measurements. Dealing with these two issues in practice relies on a physician's knowledge and the manual analysis of CPGs. Yet for guidelines to be considered stand-alone and an easy to use clinical decision support tool this process needs to be automated. In this paper we present our algorithm that aims to build such a tool by mitigating multiple CPGs while handling cycles and numerical measurements. The application of the mitigation algorithm is illustrated with a clinical case study involving a comorbid patient suffering from atrial fibrillation in the setting of Wolff-Parkinsons-White syndrome.

Practitioners and researchers in health services and public health routinely estimate population health indicators from a range of data sources. These indicators are used in many settings to describe health status, monitor quality of care, and evaluate the effect of interventions. The data and knowledge necessary to calculate indicators, however, are scattered across different health settings, resulting in inconsistent and fragmented indicators and an inefficient use of population health information in research and practice. The Population Health Record (PopHR) described in this paper is an informatics platform for semi-automated integration of disparate data to enable measurement and monitoring of population health status and determinants. The research and development to build the PopHR uses AI methods to perform many tasks, including calculation of indicators and interaction with users.

In this paper we offer an approach for reasoning about resource allocation and scheduling in multiagent systems that takes into consideration the costs of preempting an agent from its current task. We apply our methodology to the motivating medical application of allocating doctors to patients in hospitals during mass casualty incidents and demonstrate noticeable improvements in performance (generating far fewer problem patients) over competing approaches that do not model the costs of preemption in sufficient detail. In particular, our approach offers a method for addressing the challenges of cyclical dependencies in the estimation of preemption costs by localized agents through a combination of planning techniques.

Multi-disciplinary shared care is based around a single, patient-centred health record. A key driver for storing that record electronically is the need to gather data once (for clinical care) and to reuse it for secondary purposes, including clinical studies. However, physicians working in different specialties may have different perspectives on that record, both when entering new data for clinical use and when reusing those data in clinical studies. The ORCHID classification scheme in use at the Nottingham University Hospitals NHS Trust in the UK, is an ontology-based model which supports multiple, simultaneous clinical perspectives yet allows data to be stored as standard HL7 CDA documents in an immutable, patient-centred record. This paper describes the basic mechanisms used to support those multiple perspectives and the solution to specific problems of recording diagnosis with co-morbidities and recording different levels of detail in disease phenotypes.