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Machine Learning and Robot Perception - Bruno Apolloni et al (Eds) Part 15

Tham khảo tài liệu 'machine learning and robot perception - bruno apolloni et al (eds) part 15', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 8 Cognitive User Modeling Computed by a Proposed Dialogue Strategy 345 Therefore the prospective dialogue strategy taken by player1 is computed by taking into account the following probabilistic noise as follows b - bk M0 15 where b can be obtained by shifting the original pay-off matrix in Eq. 6 . We suggest that the addictive noise effect to the pay-off may play a crucial role in the stability of dialogue strategies and prevents in particular from having to use dead reckoning. This could also be reminiscent of a regularization effect in machine learning theory. In practice the regularization has been applied in wide range of sensory technology. In our case the proposed dialogue strategy incorporating Eq. 15 in our dialogue strategy is capable of having real world competence. This may be true for intelligent sensory technology for instance proposed by Koshizen 2002 . Such a technology learns cross-correlation among different sensors - selecting sensors that can be the best ones for minimizing a predictive localization error of a robot by modeling the uncertainty Koshizen 2001 . That means probabilistic models computed by sonar and infrared sensors were employed to estimate each robot s location. Figures 8.6-8.7 describes the computational aspect resulting from several simulations considering the proposed dialogue strategy type 2 implicates when player choose their dialogue actions statistically subject to the approximation of true pay-off matrix. Altogether the players interacted 5000 times. Interactions were made of 100 sets and each set consisted of 50 steps. The initial value of possible numbers of the pay-off matrix was 1000 points. All components of the pay-off matrix were normalized. The plotted points represent dialogue actions which were taken by player1 during their interactions. The rule of their interactions was assumed to follow the context of the modified IPD game. As a result the actual pay-off matrix of player2 was cooperative so a pay-off matrix .