Climate change has been a popular topic for a number of years now. Computer Science has contributed to aiding humanity in reducing energy requirements and consequently global warming. Much of this work is through calculators which determine a user’s carbon footprint. However there are no expert systems which can offer advice in an efficient and time saving way. There are many publications which do offer advice on reducing greenhouse gas (GHG) emissions but to find the advice the reader seeks will involve reading a lot of irrelevant material. This work built an expert system (which we call EcoHomeHelper) and attempted to show that it is useful in changing people’s behaviour with respect to their GHG emissions and that they will be able to find the information in a more efficient manner. Twelve participants were used. Seven of which used the program and five who read and attempted to find advice by reading from a list. The application itself has current implementations and the concept further developed, has applications for the future.
It may appear strange to some readers to be considering Social Psychology issues when attempting Computer Science research. However, this author believes the union is at times inevitable and indeed unavoidable when building an interactive application offering advise. In discussing Human-Computer Interaction (HCI), from @cite_6 we can confidently conclude that people do form social-emotional relationships with such things as jewelry, clothing and other non humanoid physical or nonphysical forms. The authors further suggest that people can respond to computers in social ways even though they may be unconscious of this behaviour and consequently form relationships with computers. The above article continues to argue that due to this social-emotional bonding with the agent, in this case the application, computers can play a role in aiding people to change their behaviour. In other words, computer applications can play a role in persuasion.
@cite_6
1985945240 2142653084 2168053878 2103075707
This research investigates the meaning of “human-computer relationship” and presents techniques for constructing, maintaining, and evaluating such relationships, based on research in social psychology, sociolinguistics, communication and other social sciences. Contexts in which relationships are particularly important are described, together with specific benefits (like trust) and task outcomes (like improved learning) known to be associated with relationship quality. We especially consider the problem of designing for long-term interaction, and define relational agents as computational artifacts designed to establish and maintain long-term social-emotional relationships with their users. We construct the first such agent, and evaluate it in a controlled experiment with 101 users who were asked to interact daily with an exercise adoption system for a month. Compared to an equivalent task-oriented agent without any deliberate social-emotional or relationship-building skills, the relational agent was respected more, liked more, and trusted more, even after four weeks of interaction. Additionally, users expressed a significantly greater desire to continue working with the relational agent after the termination of the study. We conclude by discussing future directions for this research together with ethical and other ramifications of this work for HCI designers. Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. However, one necessary ingredient for natural interaction is still missing - emotions. This paper describes the problem of bimodal emotion recognition and advocates the use of probabilistic graphical models when fusing the different modalities. We test our audio-visual emotion recognition approach on 38 subjects with 11 HCI-related affect states. The experimental results show that the average person-dependent emotion recognition accuracy is greatly improved when both visual and audio information are used in classification. The ability of a computer to detect and appropriately respond to changes in a user's affective state has significant implications to human-computer interaction (HCI). In this paper, we present our efforts toward audio-visual affect recognition on 11 affective states customized for HCI application (four cognitive motivational and seven basic affective states) of 20 nonactor subjects. A smoothing method is proposed to reduce the detrimental influence of speech on facial expression recognition. The feature selection analysis shows that subjects are prone to use brow movement in face, pitch and energy in prosody to express their affects while speaking. For person-dependent recognition, we apply the voting method to combine the frame-based classification results from both audio and visual channels. The result shows 7.5 improvement over the best unimodal performance. For person-independent test, we apply multistream HMM to combine the information from multiple component streams. This test shows 6.1 improvement over the best component performance In human-computer collaboration involving haptics, a key issue that remains to be solved is to establish an intuitive communication between the partners. Even though computers are widely used to aid human operators in teleoperation, guidance, and training, because they lack the adaptability, versatility, and awareness of a human, their ability to improve efficiency and effectiveness in dynamic tasks is limited. We suggest that the communication between a human and a computer can be improved if it involves a decision-making process in which the computer is programmed to infer the intentions of the human operator and dynamically adjust the control levels of the interacting parties to facilitate a more intuitive interaction setup. In this paper, we investigate the utility of such a dynamic role exchange mechanism, where partners negotiate through the haptic channel to trade their control levels on a collaborative task. We examine the energy consumption, the work done on the manipulated object, and the joint efficiency in addition to the task performance. We show that when compared to an equal control condition, a role exchange mechanism improves task performance and the joint efficiency of the partners. We also show that augmenting the system with additional informative visual and vibrotactile cues, which are used to display the state of interaction, allows the users to become aware of the underlying role exchange mechanism and utilize it in favor of the task. These cues also improve the user's sense of interaction and reinforce his her belief that the computer aids with the execution of the task.