Awareness Based Recommendation

2019 ◽  
pp. 167-186
Author(s):  
Tomohiro Yamaguchi ◽  
Takuma Nishimura ◽  
Keiki Takadama
Keyword(s):  
Artificial Intelligence ◽  
Interaction Design ◽  
Interactive Learning ◽  
Learning System ◽  
Experimental Results ◽  
Human Intelligence ◽  
Human Interface ◽  
True Preference ◽  
Heavy Users ◽  
And Robotics

In Artificial Intelligence and Robotics, one of the important issues is to design Human interface. There are two issues, one is the machine-centered interaction design to adapt humans for operating the robots or systems. Another one is the human-centered interaction design to make it adaptable for humans. This research aims at latter issue. This paper presents the interactive learning system to assist positive change in the preference of a human toward the true preference, then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the authors' system improves the efficiency for deciding the most preferred plan for both heavy users and light users.

Awareness Based Recommendation

2016 ◽  
Vol 4 (1) ◽  
pp. 83-99
Author(s):  
Tomohiro Yamaguchi ◽  
Takuma Nishimura ◽  
Keiki Takadama
Keyword(s):  
Artificial Intelligence ◽  
Interaction Design ◽  
Interactive Learning ◽  
Learning System ◽  
Experimental Results ◽  
Human Intelligence ◽  
Human Interface ◽  
True Preference ◽  
Heavy Users ◽  
And Robotics

In Artificial Intelligence and Robotics, one of the important issues is to design Human interface. There are two issues, one is the machine-centered interaction design to adapt humans for operating the robots or systems. Another one is the human-centered interaction design to make it adaptable for humans. This research aims at latter issue. This paper presents the interactive learning system to assist positive change in the preference of a human toward the true preference, then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the authors' system improves the efficiency for deciding the most preferred plan for both heavy users and light users.

Awareness-Based Recommendation Toward a New Preference

2019 ◽  
pp. 572-593
Author(s):  
Tomohiro Yamaguchi ◽  
Takuma Nishimura ◽  
Keiki Takadama
Keyword(s):  
Interaction Design ◽  
Design Research ◽  
Learning System ◽  
Human Intelligence ◽  
Human Interface ◽  
True Preference ◽  
Heavy Users ◽  
And Training ◽  
And Robotics ◽  
The Way

In mechatronics and robotics, one of the important issues is to design human interface. There are two issues on interaction design research. One is the way to education and training to adapt humans for operating the robots or interaction systems. Another one is the way to make interaction design adaptable for humans. This chapter research at the latter issue. This chapter describes the interactive learning system to assist positive change in the preference of a human toward the true preference; then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the system improves the efficiency for deciding the most preferred plan for both heavy users and light users.

Awareness-Based Recommendation

2013 ◽  
pp. 86-100
Author(s):  
Tomohiro Yamaguchi ◽  
Takuma Nishimura ◽  
Keiki Takadama
Keyword(s):  
Reinforcement Learning ◽  
Interaction Design ◽  
Interactive Learning ◽  
Robot Learning ◽  
Learning System ◽  
Experimental Results ◽  
Interactive System ◽  
Human Intelligence ◽  
True Preference ◽  
Heavy Users

This chapter describes the interactive learning system to assist positive change in the preference of a human toward the true preference. First, an introduction to interactive reinforcement learning with human in robot learning is given; then, the need to estimate the human’s preference and to consider its changes by interactive learning system is described. Second, requirements for interactive system as being human adaptive and friendly are discussed. Then, the passive interaction design of the system to assist the awareness for a human is proposed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results show that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also show that the system improves the efficiency for deciding the most preferred plan for both heavy users and light users.

Awareness-Based Recommendation toward a New Preference

2015 ◽  
pp. 219-241
Author(s):  
Tomohiro Yamaguchi ◽  
Takuma Nishimura ◽  
Keiki Takadama
Keyword(s):  
Interaction Design ◽  
Design Research ◽  
Learning System ◽  
Human Intelligence ◽  
Human Interface ◽  
True Preference ◽  
Heavy Users ◽  
And Training ◽  
And Robotics ◽  
The Way

In mechatronics and robotics, one of the important issues is to design human interface. There are two issues on interaction design research. One is the way to education and training to adapt humans for operating the robots or interaction systems. Another one is the way to make interaction design adaptable for humans. This chapter research at the latter issue. This chapter describes the interactive learning system to assist positive change in the preference of a human toward the true preference; then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the system improves the efficiency for deciding the most preferred plan for both heavy users and light users.

Awareness-Based Recommendation by Passively Interactive Learning

2018 ◽  
pp. 247-275
Author(s):  
Tomohiro Yamaguchi ◽  
Takuma Nishimura ◽  
Shota Nagahama ◽  
Keiki Takadama
Keyword(s):  
Interaction Design ◽  
Interactive Learning ◽  
Learning System ◽  
Future Research ◽  
Research Directions ◽  
Human Interface ◽  
True Preference ◽  
Heavy Users ◽  
Future Research Directions ◽  
And Robotics

In artificial intelligence and robotics, one of the important issues is to design human interface. There are two issues: One is the machine-centered interaction design. Another one is the human-centered interaction design. This research aims at the latter issue. This chapter presents the interactive learning system to assist positive change in the preference of a human toward the true preference. Then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the authors' system improves the efficiency for deciding the most preferred plan for both heavy users and light users. As future research directions, a probabilistic event and its basic recommendation way are discussed.

Human and Artificial Intelligence: A Clarification

2021 ◽  
pp. 13-48
Author(s):  
Thomas Fuchs
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Human Person ◽  
The Other ◽  
Human Intelligence ◽  
The Difference ◽  
The One ◽  
And Robotics

The advances in artificial intelligence (AI) and robotics are increasingly calling into question the distinction between the simulation and reality of the human person. On the one hand, they suggest a computeromorphic understanding of human intelligence, and on the other, an anthropomorphic view of AI systems. In other words: we increasingly view ourselves as our machines, and conversely, our machines as ourselves. So, what is the difference between human and AI? And can AI achieve consciousness at some point? The chapter argues that an embodied view of consciousness and the person establishes a notion of intelligence that cannot be reduced to information processing.

Intelligence, Artificial and Otherwise

2019 ◽  
Vol 24 (2) ◽  
pp. 241-258
Author(s):  
Paul Dumouchel
Keyword(s):  
Artificial Intelligence ◽  
Human Intelligence

The idea of artificial intelligence implies the existence of a form of intelligence that is “natural,” or at least not artificial. The problem is that intelligence, whether “natural” or “artificial,” is not well defined: it is hard to say what, exactly, is or constitutes intelligence. This difficulty makes it impossible to measure human intelligence against artificial intelligence on a unique scale. It does not, however, prevent us from comparing them; rather, it changes the sense and meaning of such comparisons. Comparing artificial intelligence with human intelligence could allow us to understand both forms better. This paper thus aims to compare and distinguish these two forms of intelligence, focusing on three issues: forms of embodiment, autonomy and judgment. Doing so, I argue, should enable us to have a better view of the promises and limitations of present-day artificial intelligence, along with its benefits and dangers and the place we should make for it in our culture and society.

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