Showing items 1 - 4 of 4

A fully automated CAD system using multi-category feature selection with restricted recombination

- Ghosh, Ranadhir, Ghosh, Moumita, Yearwood, John, Mukherjee, Subhasis

  • Authors: Ghosh, Ranadhir , Ghosh, Moumita , Yearwood, John , Mukherjee, Subhasis
  • Date: 2007
  • Type: Text , Conference paper
  • Relation: Paper presented at 6th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2007, Melbourne, Victoria : 11th-13th July 2007 p. 106-111
  • Full Text:
  • Description: In pattern recognition problems features plays an important role for classification results. It is very important which features are used and how many features are used for the classification process. Most of the real life classification problem uses different category of features. It is desirable to find the optimal combination of features that improves the performance of the classifier. There exists different selection framework that selects the features. Mostly do not incorporate the impact of one category of features on another. Even if they incorporate, they produce conflict between the categories. In this paper we proposed a restricted crossover selection framework which incorporate the impact of different categories on each other, as well as it restricts the search within the category which searching in the global region of the search space. The results obtained by the proposed framework are promising.
  • Description: 2003005429

A reinforcement learning approach with spline-fit object tracking for AIBO Robot's high level decision making

- Mukherjee, Subhasis, Huda, Shamsul, Yearwood, John

  • Authors: Mukherjee, Subhasis , Huda, Shamsul , Yearwood, John
  • Date: 2011
  • Type: Text , Book chapter
  • Relation: Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing p. 169-183
  • Full Text: false
  • Reviewed:
  • Description: Robocup is a popular test bed for AI programs around the world. Robosoccer is one of the two major parts of Robocup, in which AIBO entertainment robots take part in the middle sized soccer event. The three key challenges that robots need to face in this event are manoeuvrability, image recognition and decision making skills. This paper focuses on the decision making problem in Robosoccer-The goal keeper problem. We investigate whether reinforcement learning (RL) as a form of semi-supervised learning can effectively contribute to the goal keeper's decision making process when penalty shot and two attacker problem are considered. Currently, the decision making process in Robosoccer is carried out using rule-base system. RL also is used for quadruped locomotion and navigation purpose in Robosoccer using AIBO. Moreover the ball distance is being calculated using IR sensors available at the nose of the robot. In this paper, we propose a reinforcement learning based approach that uses a dynamic state-action mapping using back propagation of reward and Q-learning along with spline fit (QLSF) for the final choice of high level functions in order to save the goal. The novelty of our approach is that the agent learns while playing and can take independent decision which overcomes the limitations of rule-base system due to fixed and limited predefined decision rules. The spline fit method used with the nose camera was also able to find out the location and the ball distance more accurately compare to the IR sensors. The noise source and near and far sensor dilemma problem with IR sensor was neutralized using the proposed spline fit method. Performance of the proposed method has been verified against the bench mark data set made with Upenn'03 code logic and a base line experiment with IR sensors. It was found that the efficiency of our QLSF approach in goalkeeping was better than the rule based approach in conjunction with the IR sensors. The QLSF develops a semi-supervised learning process over the rule-base system's input-output mapping process, given in the Upenn'03 code. © 2011 Springer-Verlag Berlin Heidelberg.

Applying reinforcement learning in playing Robosoccer using the AIBO

- Mukherjee, Subhasis

  • Authors: Mukherjee, Subhasis
  • Date: 2010
  • Type: Text , Thesis , Masters
  • Full Text:
  • Description: "Robosoccer is a popular test bed for AI programs around the world in which AIBO entertainments robots take part in the middle sized soccer event. These robots need a variety of skills to perform in a semi-real environment like this. The three key challenges are manoeuvrability, image recognition and decision making skills. This research is focussed on the decision making skills ... The work focuses on whether reinforcement learning as a form of semi supervised learning can effectively contribute to the goal keeper's decision making when a shot is taken." -
  • Description: Master of Computing (by research)

Reinforcement learning approach to AIBO robot's decision making process in Robosoccer's goal keeper problem

- Mukherjee, Subhasis, Yearwood, John, Vamplew, Peter, Huda, Shamsul

  • Authors: Mukherjee, Subhasis , Yearwood, John , Vamplew, Peter , Huda, Shamsul
  • Date: 2011
  • Type: Text , Conference proceedings
  • Full Text: false
  • Description: Robocup is a popular test bed for AI programs around the world. Robosoccer is one of the two major parts of Robocup, in which AIBO entertainment robots take part in the middle sized soccer event. The three key challenges that robots need to face in this event are manoeuvrability, image recognition and decision making skills. This paper focuses on the decision making problem in Robosoccer - The goal keeper problem. We investigate whether reinforcement learning (RL) as a form of semi-supervised learning can effectively contribute to the goal keeper's decision making process when penalty shot and two attacker problem are considered. Currently, the decision making process in Robosoccer is carried out using rule-base system. RL also is used for quadruped locomotion and navigation purpose in Robosoccer using AIBO. In this paper, we propose a reinforcement learning based approach that uses a dynamic state-action mapping using back propagation of reward and space quantized Q-learning (SQQL) for the choice of high level functions in order to save the goal. The novelty of our approach is that the agent learns while playing and can take independent decision which overcomes the limitations of rule-base system due to fixed and limited predefined decision rules. Performance of the proposed method has been verified against the bench mark data set made with Upenn'03 code logic. It was found that the efficiency of our SQQL approach in goalkeeping was better than the rule based approach. The SQQL develops a semi-supervised learning process over the rule-base system's input-output mapping process, given in the Upenn'03 code. © 2011 IEEE.
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