SUMMARYThis work examines theCooperative Huntersproblem, where a swarm of unmanned air vehicles (UAVs) is used for searching one or more “evading targets,” which are moving in a predefined area while trying to avoid a detection by the swarm. By arranging themselves into efficient geometric flight configurations, the UAVs optimize their integrated sensing capabilities, enabling the search of a maximal territory.

Abstract. In the world of living creatures, "simple minded" animals often cooperate to achieve common goals with amazing performance. One can consider this idea in the context of robotics, and suggest models for programming goaloriented behavior into the members of a group of simple robots lacking global supervision. This can be done by controlling the local interactions between the robot agents, to have them jointly carry out a given mission. As a test case we analyze the problem of many simple robots cooperating to clean the dirty floor of a non-convex region in Z 2 , using the dirt on the floor as the main means of inter-robot communication.

Several recent works considered multi agents robotics in static environments (e.g.[2], [4], [5] and others). In this work we examine ways of operating in dynamic environments, in which changes may take place regardless of the agents' activity. The work focuses on a dynamic variant of the known Cooperative Cleaners problem (described and analyzed in [2]). This problem assumes a grid, part of which is "dirty", when the "dirty" part is a connected region of the grid. On this dirty region several agents move, each having the ability to "clean" the place it is located in. The dynamic variant of the problem involves a deterministic evolution of the environment, simulating a spreading contamination, or fire. A cleaning protocol for the problem is presented, as well as several analytic bounds for it. In addition, the work contains simulative results for the proposed protocol.

No abstract

No abstract

No abstract

Network planning and traffic flow optimization require the acquisition and analysis of large quantities of data such as the

Prospect theory is widely viewed as the best available descriptive model of how people evaluate risk in experimental settings. According to prospect theory, people are typically risk-averse with respect to gains and risk-seeking with respect to losses, known as the “reflection effect”. People are much more sensitive to losses than to gains of the same magnitude, a phenomenon called “loss aversion”. Despite of the fact that prospect theory has been well developed in behavioral economics at the theoretical level, there exist very few large-scale empirical studies and most of the previous studies have been undertaken with micro-panel data. Here we analyze over 28.5 million trades made by 81.3 thousand traders of an online financial trading community over 28 months, aiming to explore the large-scale empirical aspect of prospect theory. By analyzing and comparing the behavior of winning and losing trades and traders, we find clear evidence of the reflection effect and the loss aversion phenomenon, which are essential in prospect theory. This work hence demonstrates an unprecedented large-scale empirical evidence of prospect theory, which has immediate implication in financial trading, e.g., developing new trading strategies by minimizing the impact of the reflection effect and the loss aversion phenomenon. Moreover, we introduce three novel behavioral metrics to differentiate winning and losing traders based on their historical trading behavior. This offers us potential opportunities to augment online social trading where traders are allowed to watch and follow the trading activities of others, by predicting potential winners based on their historical trading behavior.