We present an approach that uses Q-learning on individual robotic agents, for coordinating a missiontasked team of robots in a complex scenario. To reduce the size of the state space, actions are grouped into sets of related behaviors called roles and represented as behavioral assemblages. A role is a Finite State Automata such as Forager, where the behaviors and their sequencing for finding ob...

In realistic multiagent systems, learning on the basis of complete state information is not feasible. We introduce adaptive state focus Q-learning, a class of methods derived from Q-learning that start learning with only the state information that is strictly necessary for a single agent to perform the task, and that monitor the convergence of learning. If lack of convergence is detected, the l...

We prove that Simulated Annealing with an appropriate cooling schedule computes arbitrarily tight constant-factor approximations to the minimum spanning tree problem in polynomial time. This result was conjectured by Wegener (2005). More precisely, denoting $n, m, w_{\max}$, and $w_{\min}$ number of vertices edges as well maximum edge weight MST instance, we simulated annealing initial temperat...

Answering a question of J. Lawson (formulated also earlier, in 1984, by Kamimura and Tang [Theoret. Comput. Sci. 34 (1984) 275–288]) we show that every Polish space admits a bounded complete computational model, as defined below. This results from our construction, in each Polish space 〈X,τ 〉, of a countable family C of non-empty closed subsets of X such that: (cp) each subset of C with the fin...

It has always been a goal in the medical sciences to develop diagnostic and imaging strategies having a minimum or no invasiveness and/or obliterating need of further invasive investigations. The differentiation of benign versus malignant tumors on the basis of imaging findings has always been a diagnostic dilemma as there is always some degree of overlap of imaging characteristics in both the ...