Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach

Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents a non-intrusive approach to monitoring by 'overhearing', where the monitored team's state is inferred (via plan-recognition) from team-members' routine communications, exchanged as part of their coordinated task execution, and observed (overheard) by the monitoring system. Key challenges in this approach include the demanding run-time requirements of monitoring, the scarceness of observations (increasing monitoring uncertainty), and the need to scale-up monitoring to address potentially large teams. To address these, we present a set of complementary novel techniques, exploiting knowledge of the social structures and procedures in the monitored team: (i) an efficient probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting knowledge of the team's social behavior to predict future observations during execution (reducing monitoring uncertainty); and (iii) monitoring algorithms that trade expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities to be represented in a single coherent entity. We present an empirical evaluation of these techniques, in combination and apart, in monitoring a deployed team of agents, running on machines physically distributed across the country, and engaged in complex, dynamic task execution. We also compare the performance of these techniques to human expert and novice monitors, and show that the techniques presented are capable of monitoring at human-expert levels, despite the difficulty of the task

"Nutritional or Hormonal", The Myth of Takotsubo Cardiomyopathy and Left Ventricular Thrombosis in Anorexia Nervosa Patients! A Case Report

Takotsubo Cardiomyopathy is a condition characterized by transient LV hypokinesis without evidence of obstructive coronary disease that is typically triggered by a stressful event. We describe a 34-year-old woman with anorexia nervosa presenting with hypotension, hypoglycemia, and tachycardia and found to have Takotsubo Cardiomyopathy. On admission, EKG showed ST depression with T wave inversion in the inferolateral leads and Q waves in the anteroseptal leads (V1-V3). The initial troponin was elevated at 1.2 ng/ml and increased to 1.94 ng/ml 4 hours later. Initial TTE showed global hypokinesisexcept for a hyperkinetic basal segment suggestive of takostsubo cardiomyopathy and an EF of 15-20%. During the course of management, subsequent TTEs revealed improved EF with resolution of the cardiomyopathy, but also the presence of an LV thrombus. The pathophysiology of Takotsubo Cardiomyopathy is not fully understood, and the impact of the hypoestrogenic state and the nutritional deficiencies on the development Takotsubo Cardiomyopathy in anorexia nervosa patients remains to be a myth

A chemotactic-based model for spatial activity recognition

Spatial activity recognition in everyday environments is particularly challenging due to noise incorporated during video-tracking. We address the noise issue of spatial recognition with a biologically inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to survive by changing motile behaviour in relation to environmental dynamics. Using chemotactic principles, we propose the chemotactic model and evaluate its classification performance in a smart house environment. The model exhibits high classification accuracy (99%) with a diverse 10 class activity dataset and outperforms the discrete hidden Markov model (HMM). High accuracy (>89%) is also maintained across small training sets and through incorporation of varying degrees of artificial noise into testing sequences. Importantly, unlike other bottom–up spatial activity recognition models, we show that the chemotactic model is capable of recognizing simple interwoven activities

Ring-shaped variation of the coeliac trunk branches

Aberrant arterial variations in the branching pattern of the coeliac trunk are of great interest to surgeons and radiologists. We report on a rare arterial variation found in a 79-year-old cadaver during educational dissection. Specifically, the coeliac axis formed a unique incomplete trunk termed the hepato-hepatic trunk. The splenic artery arose separately from the anterior aspect of the abdominal aorta. On the right side, there was a right hepatic artery giving rise to a gastroduodenal but an absence of the left hepatic. On the left side, there was a branch coursing towards the porta hepatis; the left hepatic artery, dividing into the left gastric, an accessory left gastric, and a branch to the distal oesophagus. The hepato-hepatic trunk formed a ring-shaped vascular structure around the caudate lobe of the liver. Precise mapping and observation of the extrahepatic arteries and bile duct branches is essential in a variety of hepato-biliary laparoscopic procedures of the liver and gallbladder. Other operative procedures requiring, a comprehensive kno­wledge of the varied coeliac trunk patterns are liver transplantation and arterial embolism for hepatic tumour therapy

Human–agent collaboration for disaster response

In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a multi-agent Markov decision process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked

Can bounded and self-interested agents be teammates? Application to planning in ad hoc teams

Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of self-interested decision-making frameworks. Agents engaged in individual decision making in multiagent settings face the task of having to reason about other agents’ actions, which may in turn involve reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models. For the purposes of this study, individual, self-interested decision making in multiagent settings is modeled using interactive dynamic influence diagrams (I-DID). These are graphical models with the benefit that they naturally offer a factored representation of the problem, allowing agents to ascribe dynamic models to others and reason about them. We demonstrate that an implication of bounded, finitely-nested reasoning by a self-interested agent is that we may not obtain optimal team solutions in cooperative settings, if it is part of a team. We address this limitation by including models at level 0 whose solutions involve reinforcement learning. We show how the learning is integrated into planning in the context of I-DIDs. This facilitates optimal teammate behavior, and we demonstrate its applicability to ad hoc teamwork on several problem domains and configurations

On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks

This paper describes the student modeling component of ANDES, an Intelligent Tutoring System for Newtonian physics. ANDES' student model uses a Bayesian network to do long-term knowledge assessment, plan recognition and prediction of students' actions during problem solving. The network is updated in real time, using an approximate anytime algorithm based on stochastic sampling, as a student solves problems with ANDES.The information in the student model is used by ANDES' Help system to tailor its support when the student reaches impasses in the problem solving process. In this paper, we describe the knowledge structures represented in the student model and discuss the implementation of the Bayesian network assessor. We also present a preliminary evaluation of the time performance of stochastic sampling algorithms to update the network