In many problems in the area of artificial intelligence, it is necessary to deal with uncertainty. Using probabilistic models can also improve efficiency of standard AI-based techniques. Commonly used methods for dealing with uncertainty include Bayesian models, which can be used to describe and work with probabilistic systems effectively. This article reviews several models based on the Bayesi...

We describe a general and exact method to considerably speed up linear object detection systems operating in a sliding, multi-scale window fashion, such as the individual part detectors of part-based models. The main bottleneck of many of those systems is the computational cost of the convolutions between the multiple rescalings of the image to process, and the linear filters. We make use of pr...

The aggregation of image statistics – the so-called pooling step of image classification algorithms – as well as the construction of part-based models, are two distinct and well-studied topics in the literature. The former aims at leveraging a whole set of local descriptors that an image can contain (through spatial pyramids or Fisher vectors for instance) while the latter argues that only a fe...

The inevitable growth in computer field brought a significant increase for storing large amount of images along with different kinds of complexity aspects. The need for efficient storage and retrieval of images has been recognized by having large image collections in the areas such as medicine, journalism, education, entertainment, advertisement, commerce, etc. The process of retrieving desired...

Researchers embracing agent-based microsimulation models often criticize the inability of urban equilibrium models to reflect spatial heterogeneity and transitional dynamics. These limitations are major barriers for land use and transportation planners who may wish to apply urban economic models in regular planning processes. To this end, this paper extends Anas’ spatial computable general equi...

This work proposes a Metascheduler for GRID platforms based on the Interaction Protocols of the Multiagent Systems. These protocols use the paradigm of economic models to define the coordination mechanisms in agent communities. Specifically, in this work we use auction and tender economical models. We propose an adaptive Metascheduler for GRID platforms using these ideas. According to the numbe...

Are current economic models well equipped to provide useful policy prescriptions? Many economists would have certainly answered, “yes” before the recent Global Recession. This economic crisis has not only demonstrated the importance of banking and financial markets for the dynamics of real economies. It has also revealed the inadequacy of the dominant theoretical framework. Standard models have...

The evolution of unconditional cooperation is one of the fundamental problems in science. A new solution is proposed to solve this puzzle. We treat this issue with an evolutionary model in which agents play the Prisoner’s Dilemma on signed networks. The topology is allowed to co-evolve with relational signs as well as with agent strategies. We introduce a strategy that is conditional on the emo...

Research in shared mental models has immeasurably aided our understanding of effective teamwork and taskwork. However, little research has focused on the role that leaders play, if any, in influencing, developing and/or fostering shared mental models and thereby improving team performance. We developed an agent-based computational model based on McComb's theory of three-phase mental model devel...

The recent advancement of agent-based modeling and simulation has been revolutionizing the social sciences, and the expectations are rising in social sciences. In the current state in the study of simulating agentbased economic models, however, there are some problems, as follows, that need to be resolved: (1) absence of integrated environment to support a whole research process, (2) difficulty...