Intelligent Agents with Reinforcement Learning and Fuzzy logic for Intention commitment Modeling

Artificial intelligence evolves rapidly and will have a great impact on the society in the future. One important question which still cannot be addressed with satisfaction is whether the decision of an intelligent agent can be predicted. As a consequence of this, the general question arises if such agents can be controllable and future robotic applications can be safe. This chapter shows that unpredictable systems are very common in mathematics and physics although the underlying mathematical structure can be very simple. It also shows that such unpredictability can also emerge for intelligent agents in reinforcement learning, especially for complex tasks with various input parameters. An observer would not be capable to distinguish this unpredictability from a free will of the agent. This raises ethical questions and safety issues which are briefly presented.

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Fuzzy Logic Based Intelligent Agents for Unmanned Combat Aerial Vehicle Control

Journal of Defense Management ◽

10.4172/2167-0374.1000139 ◽

2016 ◽

Vol 06 (01) ◽

Cited By ~ 5

Author(s):

Nick Ernest ◽

Kelly Cohen

Keyword(s):

Fuzzy Logic ◽

Intelligent Agents ◽

Vehicle Control ◽

Aerial Vehicle

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Mobile Robot Wall-Following Control Using Fuzzy Logic Controller with Improved Differential Search and Reinforcement Learning

Mathematics ◽

10.3390/math8081254 ◽

2020 ◽

Vol 8 (8) ◽

pp. 1254 ◽

Cited By ~ 1

Author(s):

Cheng-Hung Chen ◽

Shiou-Yun Jeng ◽

Cheng-Jian Lin

Keyword(s):

Fuzzy Logic ◽

Reinforcement Learning ◽

Mobile Robot ◽

Fuzzy Logic Controller ◽

Search Algorithm ◽

Experimental Results ◽

Average Error ◽

Stopover Site ◽

Accumulated Reward ◽

Wall Following

In this study, a fuzzy logic controller with the reinforcement improved differential search algorithm (FLC_R-IDS) is proposed for solving a mobile robot wall-following control problem. This study uses the reward and punishment mechanisms of reinforcement learning to train the mobile robot wall-following control. The proposed improved differential search algorithm uses parameter adaptation to adjust the control parameters. To improve the exploration of the algorithm, a change in the number of superorganisms is required as it involves a stopover site. This study uses reinforcement learning to guide the behavior of the robot. When the mobile robot satisfies three reward conditions, it gets reward +1. The accumulated reward value is used to evaluate the controller and to replace the next controller training. Experimental results show that, compared with the traditional differential search algorithm and the chaos differential search algorithm, the average error value of the proposed FLC_R-IDS in the three experimental environments is reduced by 12.44%, 22.54% and 25.98%, respectively. Final, the experimental results also show that the real mobile robot using the proposed method can effectively implement the wall-following control.

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