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

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

My Actions Speak Louder Than Your Words: When User Behavior Predicts Their Beliefs about Agents' Attributes

An implicit expectation of asking users to rate agents, such as an AI decision-aid, is that they will use only relevant information -- ask them about an agent's benevolence, and they should consider whether or not it was kind. Behavioral science, however, suggests that people sometimes use irrelevant information. We identify an instance of this phenomenon, where users who experience better outcomes in a human-agent interaction systematically rated the agent as having better abilities, being more benevolent, and exhibiting greater integrity in a post hoc assessment than users who experienced worse outcome -- which were the result of their own behavior -- with the same agent. Our analyses suggest the need for augmentation of models so that they account for such biased perceptions as well as mechanisms so that agents can detect and even actively work to correct this and similar biases of users.Comment: HCII 202

Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning

We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team's behavior during some task. Unlike current MIRL approaches, we do not assume that team members know each other's goals a priori; rather, that they collaborate by adapting to the goals of others perceived by observing their behavior, all while jointly performing a task. To address this problem, we propose a novel approach to MIRL via Theory of Mind (MIRL-ToM). For each agent, we first use ToM reasoning to estimate a posterior distribution over baseline reward profiles given their demonstrated behavior. We then perform MIRL via decentralized equilibrium by employing single-agent Maximum Entropy IRL to infer a reward function for each agent, where we simulate the behavior of other teammates according to the time-varying distribution over profiles. We evaluate our approach in a simulated 2-player search-and-rescue operation where the goal of the agents, playing different roles, is to search for and evacuate victims in the environment. Our results show that the choice of baseline profiles is paramount to the recovery of the ground-truth rewards, and that MIRL-ToM is able to recover the rewards used by agents interacting both with known and unknown teammates.Comment: Accepted as a full paper at AAMAS202

"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

Gradient-based Discrete Sampling with Automatic Cyclical Scheduling

Discrete distributions, particularly in high-dimensional deep models, are often highly multimodal due to inherent discontinuities. While gradient-based discrete sampling has proven effective, it is susceptible to becoming trapped in local modes due to the gradient information. To tackle this challenge, we propose an automatic cyclical scheduling, designed for efficient and accurate sampling in multimodal discrete distributions. Our method contains three key components: (1) a cyclical step size schedule where large steps discover new modes and small steps exploit each mode; (2) a cyclical balancing schedule, ensuring ``balanced" proposals for given step sizes and high efficiency of the Markov chain; and (3) an automatic tuning scheme for adjusting the hyperparameters in the cyclical schedules, allowing adaptability across diverse datasets with minimal tuning. We prove the non-asymptotic convergence and inference guarantee for our method in general discrete distributions. Extensive experiments demonstrate the superiority of our method in sampling complex multimodal discrete distributions

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