Multi-Agent Word Guessing Game

The task of creating algorithms to solve a problem is surely a hard thing as it can be the fact of evaluating them. A well designed algorithm can be very powerful but, it may lack of efficiency at some aspects. This paper proposes a multi-agent system based game with three types of agents: CBot, ABot and QBot, which stands for Coordinator, Answer and Question. They will play a game based on questions and answers, where each of the QBots uses a different algorithm to guess a word. The CBot has the responsibility of the efficiency measurements, receiving and manipulating the ABot reports. The game will finish once all QBots give the correct answer and after that, the efficiency of the algorithms thanks to the CBot. Using this method, it is easier to determine which algorithm is the best with a given performance measurement

Improving Image Captioning by Leveraging Knowledge Graphs

We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning. The results of our experiments, on several benchmark data sets such as MS COCO, as measured by CIDEr-D, a performance metric for image captioning, show that the variants of the state-of-the-art methods for image captioning that make use of the information extracted from knowledge graphs can substantially outperform those that rely solely on the information extracted from images.Comment: Accepted by WACV'1