Data intensive scientific analysis with grid computing

At the end of September 2009, a new Italian GPS receiver for radio occultation was launched from the Satish Dhawan Space Center (Sriharikota, India) on the Indian Remote Sensing OCEANSAT-2 satellite. The Italian Space Agency has established a set of Italian universities and research centers to implement the overall processing radio occultation chain. After a brief description of the adopted algorithms, which can be used to characterize the temperature, pressure and humidity, the contribution will focus on a method for automatic processing these data, based on the use of a distributed architecture. This paper aims at being a possible application of grid computing for scientific research

Policy Management across Multiple Platforms and Application Domains

One of the challenges of building a policy management framework is making it flexible enough to handle differences in both policy semantics and enforcement strategies across multiple platforms and application domains. The system must be expressive enough in each application domain to provide the richness needed for interesting policies. It must also provide a simple and flexible enforcement mechanism for adaptation to a variety of systems. In this paper we discuss the application of the KAoS policy services framework to human-robot teamwork—an application that involves a variety of application domains and enforcement at different levels of control; from low level network resource control to high level organizational constraints and coordination management. The study culminated in an outdoor field exercise that required coordination of mixed sub teams composed of two people and five robots whose task was to find and apprehend an intruder on a Navy pier. 1

An attitude based modeling of agents in coalition

One of the main underpinning of the multi-agent systems community is how and why autonomous agents should cooperate with one another. Several formal and computational models of cooperative work or coalition are currently developed and used within multi-agent systems research. The coalition facilitates the achievement of cooperation among different agents. In this paper, a mental construct called attitude is proposed and its significance in coalition formation in a dynamic fire world is discussed. This paper presents ABCAS (Attitude Based Coalition Agent System) that shows coalitions in multi-agent systems are an effective way of dealing with the complexity of fire world. It shows that coalitions explore the attitudes and behaviors that help agents to achieve goals that cannot be achieved alone or to maximize net group utility

An agent-based intelligent environmental monitoring system

Fairly rapid environmental changes call for continuous surveillance and on-line decision making. There are two main areas where IT technologies can be valuable. In this paper we present a multi-agent system for monitoring and assessing air-quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air-quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data-driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real-world test case is demonstrated.Comment: Multi-Agent Systems, Intelligent Applications, Data Mining, Inductive Agents, Air-Quality Monitorin

Detecting and avoiding interference between goals in intelligent agents

Pro-active agents typically have multiple simultaneous goals. These may interact with each other both positively and negatively. In this paper we provide a mechanism allowing agents to detect and avoid a particular kind of negative interaction where the effects of one goal undo conditions that must be protected for successful pursuit of another goal. In order to detect such interactions we maintain summary information about the definite and potential conditional requirements and resulting effects of goals and their associated plans. We use these summaries to guard protected conditions by scheduling the execution of goals and plan steps. The algorithms and data structures developed allow agents to act rationally instead of blindly pursuing goals that will conflict

Analysis of Dynamic Task Allocation in Multi-Robot Systems

Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of task, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of the collective behavior. Specifically, we analyze the effect that the number of observations and the choice of the decision function have on the performance of the system. The mathematical models are validated in a multi-robot multi-foraging scenario. The model's predictions agree very closely with experimental results from sensor-based simulations.Comment: Preprint version of the paper published in International Journal of Robotics, March 2006, Volume 25, pp. 225-24