AI and Robotics Research

This assignment delves into the rapidly evolving field of AI and Robotics, focusing on key concepts like autonomy in instruction and the challenges faced in their development. It examines how AI is used in robotics, particularly in areas such as compliant motion strategies and learning from easy missions. The paper also discusses the potential impact of AI and robotics on businesses and society, highlighting the need for further research and development in both physical and virtual worlds.

Running head: RDIT
Research and Development in Information System
Name of the student:
Name of the university:
Author Note

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Literature Review on Autonomy in AI instruction
Introduction:
AI or Artificial Intelligence is a generic term implying usage of computers for modeling or
replicating intelligent behavior. Robotics, branch of AI, is composed of various types of engineering
to design, develop and apply robots in a real-world scenario. The paper will focus on concepts and
ideas on AI and Robotics under Research and Development in Information Technology.
Key concepts and ideas:
The idea of AI and robotics has focused on computer reasoning. However, the possible
establishment of information portrayal has been lying on limited development for a machine to
understand orders. RoboCop, a famous company encouraging AI has been analyzing and including
various advancements (Kamishima, Gremmen and Akizawa 2018). Further, the study has helped in
understanding formal approach towards the synthesis of compliant-motion strategies from precise
error estimation and geometric description of various assembly operations.
Summary:
It is argued that international process is suitable to design robots in sectors of a corporative
domain. The idea of a cognitive state of an agent shows beliefs about the external world, goals and
desires to change the environment. Under cooperative cases, different reasoning regarding those
cognitive structures has been vital to solving problems and communication (Wachter, Mittelstadt and
Floridi 2017). To construct those models, experimental robot psychology is proposed. This is
because it includes reasoning and formalizing about the design of current robot agents.

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Further active compliances have been helping robots to do tasks under essential control
errors and sensing. Different compliant motions have been hard for human beings for specifying.
Robot programs have been compassionate towards the details of geometry and error characteristics
(Khandelwal et al. 2017). Thus they must be developed anew for every task. All these factors have
been motivating look for automatic synthesis tools regarding robot program and compliant motion.
Lastly, it is seen for speeding up learning time, a process of LEM or Learning from Easy Missions
has been implemented (Torresen 2017). It decreases learning time in linear order form an
exponential one within the size of state space.
Conclusion:
AI and robotics have been quickly coming of age since businesses are grasping the high
potential of smart machines as catalysts for substantial competitiveness and efficiency. Research and
development are to be conducted in this area regarding both physical and virtual worlds within
different phases of autonomy. Moreover, a comprehensive vision would be useful for the
government for optimizing the allocation of research capabilities in developing particular AI or
Robotics capabilities.

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References:
Kamishima, Y., Gremmen, B. and Akizawa, H., 2018. Can Merging a Capability Approach with
Effectual Processes Help Us Define a Permissible Action Range for AI Robotics
Entrepreneurship?. Philosophy of Management, pp.1-17.
Khandelwal, P., Zhang, S., Sinapov, J., Leonetti, M., Thomason, J., Yang, F., Gori, I., Svetlik, M.,
Khante, P., Lifschitz, V. and Aggarwal, J.K., 2017. Bwibots: A platform for bridging the gap
between ai and human–robot interaction research. The International Journal of Robotics
Research, 36(5-7), pp.635-659
Torresen, J., 2017. A Review of Future and Ethical Perspectives of Robotics and AI. Frontiers in
Robotics and AI, 4, p.75..
Wachter, S., Mittelstadt, B. and Floridi, L., 2017. Transparent, explainable, and accountable AI for
robotics.

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