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One of the overarching goals of robotics research is that robots ultimately coexist with people in human societies as an integral part of them. In order to achieve this goal, robots need to be accepted by people as natural partners within the society. It is therefore essential for robots to have adaptive learning mechanisms that can intelligently update a human model for effective human-robot interaction (HRI). This might be critical in interactions with elderly and disabled people in their daily activities. This research has developed and evaluated an intelligent HRI system that enables a mobile robot to learn adaptively about the behaviors and preferences of the people with whom it interacts. Various learning algorithms have been compared and a Bayesian learning mechanism has been implemented by estimating and updating a parameter set that models behaviors and preferences of people. Every time a user interacts with the robot, the model is updated. The robot then uses the model to predict future actions of its user. A variety of HRI modalities including speech recognition, sound source localization, simple natural language understanding, face detection, face recognition, and attention gaining/losing systems, along with a navigation system, have been integrated with the learning system. The integrated system has been successfully implemented on a Pioneer 3-AT mobile robot. The system has also been evaluated using 25 subjects who interacted with the robot using adaptive and non-adaptive interfaces. This study showed that adaptive interaction is preferred over non-adaptive interaction by the participants at a statistically significant level.