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

Decentralized dynamic task allocation for UAVs with limited communication range

We present the Limited-range Online Routing Problem (LORP), which involves a team of Unmanned Aerial Vehicles (UAVs) with limited communication range that must autonomously coordinate to service task requests. We first show a general approach to cast this dynamic problem as a sequence of decentralized task allocation problems. Then we present two solutions both based on modeling the allocation task as a Markov Random Field to subsequently assess decisions by means of the decentralized Max-Sum algorithm. Our first solution assumes independence between requests, whereas our second solution also considers the UAVs' workloads. A thorough empirical evaluation shows that our workload-based solution consistently outperforms current state-of-the-art methods in a wide range of scenarios, lowering the average service time up to 16%. In the best-case scenario there is no gap between our decentralized solution and centralized techniques. In the worst-case scenario we manage to reduce by 25% the gap between current decentralized and centralized techniques. Thus, our solution becomes the method of choice for our problem

Robust Agent Teams via Socially-Attentive Monitoring

Agents in dynamic multi-agent environments must monitor their peers to execute individual and group plans. A key open question is how much monitoring of other agents' states is required to be effective: The Monitoring Selectivity Problem. We investigate this question in the context of detecting failures in teams of cooperating agents, via Socially-Attentive Monitoring, which focuses on monitoring for failures in the social relationships between the agents. We empirically and analytically explore a family of socially-attentive teamwork monitoring algorithms in two dynamic, complex, multi-agent domains, under varying conditions of task distribution and uncertainty. We show that a centralized scheme using a complex algorithm trades correctness for completeness and requires monitoring all teammates. In contrast, a simple distributed teamwork monitoring algorithm results in correct and complete detection of teamwork failures, despite relying on limited, uncertain knowledge, and monitoring only key agents in a team. In addition, we report on the design of a socially-attentive monitoring system and demonstrate its generality in monitoring several coordination relationships, diagnosing detected failures, and both on-line and off-line applications

Robust Restless Bandits: Tackling Interval Uncertainty with Deep Reinforcement Learning

We introduce Robust Restless Bandits, a challenging generalization of restless multi-arm bandits (RMAB). RMABs have been widely studied for intervention planning with limited resources. However, most works make the unrealistic assumption that the transition dynamics are known perfectly, restricting the applicability of existing methods to real-world scenarios. To make RMABs more useful in settings with uncertain dynamics: (i) We introduce the Robust RMAB problem and develop solutions for a minimax regret objective when transitions are given by interval uncertainties; (ii) We develop a double oracle algorithm for solving Robust RMABs and demonstrate its effectiveness on three experimental domains; (iii) To enable our double oracle approach, we introduce RMABPPO, a novel deep reinforcement learning algorithm for solving RMABs. RMABPPO hinges on learning an auxiliary "$\lambda$-network" that allows each arm's learning to decouple, greatly reducing sample complexity required for training; (iv) Under minimax regret, the adversary in the double oracle approach is notoriously difficult to implement due to non-stationarity. To address this, we formulate the adversary oracle as a multi-agent reinforcement learning problem and solve it with a multi-agent extension of RMABPPO, which may be of independent interest as the first known algorithm for this setting. Code is available at https://github.com/killian-34/RobustRMAB.Comment: 18 pages, 3 figure

Determinants of Volume of POME Generation in Palm Oil Mills for Planning Wastewater Recovery in Biogas Energy Development

Wastewater volume is a necessary prerequisite for planning transformation to valuable resource and averting environmental degradation. This study investigated the dynamics of POME volume generation in palm oil mills in relation to types of fresh fruit bunches (FFBs), seasons, milling scales and volume of crude palm oil (CPO) produced in ADAPALMS and catchment communities, Ohaji/Egbema LGA, Imo State. The eight catchment communities of ADAPALMS were categorised into three strata in relation to the number of small-scale mills in each community (1-5mills, 6-10mills, and 11-15mills). In each stratum, a community was randomly sampled. A total of nine small-scale mills were sampled from the three sampled communities (Ohoba, Amafor and Etekwuru) in proportion to the average number of mills in each stratum. The lone medium and large scale mill (ADAPLAMS) in the study area represented the other scales of milling. For small and medium scale mills, the volume of POME generated was measured from the dimensions of the vessels where POME was stored, while that of large scale mill was obtained from industrial records. Data was analysed using multiple linear regression of SPSS. The volume of POME generated is significantly related to milling scales and volume of CPO produced (p< 0.01); R2=0.788. Within small scale mills, the volume of POME is significantly related to types of FFBs (p< 0.01), different small milling scales (p< 0.05), and volume of CPO produced (p< 0.01); R2=0.762. Thus, these independent variables are the principal determinants of POME volume generation in the area. The result has implication on the necessity of predictive models in managing the dynamics of POME volumes for efficient recovery and transformation of the wastewater to bioenergy

Ionic liquid catalyzed one pot green synthesis of isoxazolone derivatives via multicomponent reaction

459-464A series of 3-methyl-4-((3-aryl-1-phenyl-1H-pyrazol-4-yl)methylene)isoxazol-5(4H)-one derivatives have been efficiently synthesized by environmentally benign, one-pot three component condensation of substituted 1,3-diaryl-1H-pyrazole-4-carboxyaldehyde, β-keto ester and hydroxyl amine hydrochloride in the presence of ionic liquid [HNMP][HSO4] as a catalyst in ethanol. These derivatives have been synthesized by conventional, ultrasound and microwave irradiation methods. The combination of ionic liquid with ultrasound as well as microwave irradiation makes the protocol fascinating and environmentally benign. In addition, it has several benefits such as simple work-up procedure, clean reaction profile, short reaction time and good yields

Study of obstetric factors in perinatal morbidity and mortality at a tertiary centre

Background: Safe motherhood and child survival have always been a concern for the policymakers but perinatal mortality, especially stillbirths, have not received due attention. There are 5.9 million perinatal deaths worldwide, almost all of which occur in developing countries. Stillbirths account for over half of all perinatal deaths. This study was aimed to determine perinatal mortality rate and related obstetrics risk factors. Perinatal mortality is only a tip of the iceberg, morbidity being much higher. Vital statistics obtained through this study may serve an important source of information to guide the public health policy makers and health care providers in future.Methods: Present observational study was undertaken in a tertiary center to look into various maternal factors and possible cause of perinatal death. All perinatal deaths including stillbirths (SBs) and early neonatal deaths (ENNDs) within 0-7 days of birth after 28 weeks of gestation were analysed. The data was collected through a pre-designed proforma.Results: Perinatal mortality is 66.27/1000 births in our centre, where 37% were intrauterine deaths, 34% were neonatal deaths and 29% were still births. Preterm, pregnancy induced hypertension; abruptio placentae remain the most important factors for perinatal loss.Conclusions: One of the reasons for high perinatal mortality in tertiary centres is because of poor antenatal care at peripheral centres and late referrals. Early detection of obstetric complications and aggressive treatment is one of golden rule to reduce perinatal loss

Towards Social Comparison for Failure Detection

Abstract Social comparison, the process in which individuals compare their behavior and beliefs to those of other agents, is an important process in human societies. Our aim is to utilize theories of this process for synthetic agents, for the purposes of enabling social skills, teamcoordination, and greater individual agent performance. Our current focus is on individual failure detection and recovery in multi-agent settings. We present a novel approach, SOCFAD, inspired by Social Comparison Theory from social psychology. SOCFAD includes the following key novel concepts: (a) utilizing other agents the environment as information sources for failure detection, and (b) a detection and recovery method for previously undetectable failures using abductive inference based on other agents' beliefs 1

Production of Biodiesel using waste temple oil from Shani Shingnapur temple (Dist. Ahmednagar), Maharashtra, India using chemical and biological methods

In India, due to various mythological and religious reasons hundreds of devotees pour oil over the idols in Hanuman or Maruti and Shani temples. The oil once poured cannot be reutilized and was ultimately wasted. These waste temple oil from Shani Shingnapurwas used to produce biodiesel. Immobilized Pseudomonas aeruginosa was used to catalyze transesterification of waste temple oil. The cells of P.aeruginosa were immobilized within the sodium alginate. Biodiesel production and its applications were gaining popularity in recent years due to decreased petroleum based reserves. Biodiesel cost formed from waste temple oil was higher than that of fossil fuel, because of high raw material cost.To decrease the cost of biofuel, waste temple oil was used as alternative as feedstock. It has lower emission of pollutants; it is biodegradable and enhances engine lubricity. Waste temple oil contains triglycerides that were used for biodiesel production by chemical and biological method.Transesterification reaction of oil produces methyl esters that are substitutes for fatty acid alkyl biodiesel fuel. Characteristics of oil were studied such as specific gravity, viscosity, acid number, saponification number.Parameters such as temperature,oil: methanol ratio were studied and 88%, 96% of biodiesel yield was obtained with effect of temperature and oil: methanol ratio on transesterification reaction. Withaddition ofNaOH or KOH to fatty acids which formed salt known as soap,which is excellent emulsifying and cleaning agents