17,325 research outputs found
Performance and energy efficiency analysis of a Reversi player for FPGAs and General Purpose Processors
Board-game applications are frequently found in mobile devices where the computing performance and the energy budget are constrained. Since the Artificial Intelligence techniques applied in these games are computationally intensive, the applications developed for mobile systems are frequently simplistic, far from the level of equivalent applications developed for desktop computers.
Currently board games are software applications executed on General Purpose Processors. However, they exhibit a medium degree of parallelism and a custom hardware accelerator implemented on an FPGA can take advantage of that.
We have selected the well-known Reversi game as a case study because it is a very popular board game with simple rules but huge computational demands. We developed and optimized software and hardware designs for this game that apply the same classical Artificial Intelligence techniques. The applications have been executed on different representative platforms and the results demonstrate that the FPGAs implementations provide better performance, lower power consumption and, therefore, impressive energy savings. These results demonstrate that FPGAs can efficiently deal with this kind of problems
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
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