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A Study of Human-Robot Interaction
In the contemporary world, tasks require robots to work in collaboration with humans in order to execute them. This process requires communication to synchronize events, collaborate, and portray the image of a team. The aforementioned statement requires the comprehension of human communication; although robots/agents experience the world from a different perspective.
Urban search and rescue provides illustrations that portray that the individual-robot team involves several individuals and one or several robots. The individuals in this team might be positioned tenuously or operate beside the robots in the hot zone. The teams are usually disseminated but the robots occupy the hot zone. The combined effort between the robots and the individuals occur in mutually skilled state whereby both parties’ skill offers the root of how strategies, activities and predictable outcomes are discretely understood (Lison et al, 140).
There are several fundamental necessities for transforming a robot into a team player. There is a general assumption that there exists no common foundation between humans and robots, each of them possesses their own perspective. The approach adopted in agent architecture is situational awareness. This approach embraces the procedure of buttressing and upholding an understanding of authenticity and the depiction of that understanding. Modeling a robot while complying with situational awareness enables a robot to understand and reason with strewn situational awareness. This approach creates a connection between probabilistic illustrations of familiarity and logical depictions of domain understanding. The latter enables robots to infer properties, existing engagements, and the probable consequences. Through the act of modeling the implications as beliefs, they can be ascribed to any number of team members (human or robot). Creating a link between the beliefs and experiences enables the positioning of robots in instances that confine them to what is achievable.
Moreover, the adoption of topological mapping construction equips the robot with a number of domain ontologies, connecting things, locations, and milestones to activities the robot can execute. When a robot encounters an ontological model, it derives further properties for that scenario. An exemplary illustration is a car in an accident. The robot can deduce that the vehicle has windows thus is more likely to have occupants. In such circumstances the robot checks for passengers. A recurring issue in the field of artificial intelligence is the capability to appropriately capture the social organization and utilize the subsequent model to circumnavigate and .............
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