Behavior Selection Using Utility-Based Reinforcement Learning in Irregular Warfare Simulation Models

The article of record as published may be found at http://dx.doi.org/10.4018/joris.2013070105The Theory of Planned Behavior (TPB) provides a conceptual model for use in assessing behavioral intentions of humans. Agent based social simulations seek to represent the behavior of individuals in societies in order to understand the impact of a variety of interventions on the population in a given area. Previous work has described the implementation of the TPB in agent based social simulation using Bayesian networks. This paper describes the implementation of the TPB using novel learning techniques related to reinforcement learning. This paper provides case study results from an agent based simulation for behavior related to commodity consumption. Initial results demonstrate behavior more closely related to observable human behavior. This work contributes to the body of knowledge on adaptive learning behavior in agent based simulations

Free and open source tools for volunteer geographic information and geo-crowdsourcing

The availability of new mobile devices (tablets and smartphones) equipped with many sensors is changing or, better, enriching the way we monitor and sense the world that surrounds us. The internet has permeated completely not only our scientific and technological development, but also our life. Only some years ago, we used geospatial data and GIS software installed within our computers. Nowadays, data and operators are provided via the net by means of distributed and shared geoservices and a simple and powerless mobile device is enough to connect them. The possibility of interaction has become not only faster and more user friendly but also active, being individuals and communities free of adding, deleting, and changing contents in real time in the new GeoWeb2.0. This chapter explores GeoWeb2.0