For a seamless deployment of the Internet of Things (IoT), there is a need for self-organizing
solutions to overcome key IoT challenges that include data processing, resource …

Organisms modify their behavior in response to its consequences, a phenomenon referred
to as operant learning. The computational principles and neural mechanisms underlying …

Modern-day computer networks are highly diverse and dynamic, calling for fair and adaptive
network congestion control algorithms with the objective of achieving the best possible …

This article presents the red team emulation tool Lore, which uses boolean logic and trained
models to automatically select and execute red team actions. Lore improves the current state …

This paper describes the design and implementation of robotic agents for the RoboCup
Simulation 2D category that learns using a recently proposed Heuristic Reinforcement …

Multi-tier storage systems are becoming more and more widespread in the industry. They
have more tunable parameters and built-in policies than traditional storage systems, and an …

Reinforcement learning has developed as a successful learning approach for domains that
are not fully understood and that are too complex to be described in closed form. However …

This chapter presents the perspective that one promising avenue for understanding how
humans learn to weigh competing moral motivations is by decomposing the moral inference …

Abstract Partially Observable Markov Decision Processes (POMDPs) provide a rich
representation for agents acting in a stochastic domain under partial observability. POMDPs …

Mobile agent is a program that can migrate from a machine to another in a network and
perform tasks on machines that provide agent hosting capability. The agent can clone itself …