Real-World Environments

Agents that operate in the real world are normally designed to meet different criteria than those that operate in simulated environments. Agents that operate in the real world require robust perception mechanisms and are often faced with dynamic and unpredictable environments and a higher degree of complexity than they might encounter otherwise.

The real world provides the agent with numerous potential challenges. The agent's sensors and effectors may be imperfect, it may be required to produce new plans based on updated information very rapidly, and it might have to reason about the temporal aspects of its plans.

All of these problems are avoided by the use of simulators, which frees researchers to focus on higher-level cognitive functions such as learning and planning. However, it may be that the solutions to these lower-level problems need to arise from within the architecture rather than from outside of it, which would have a profound impact on the ultimate architecture design. If this is indeed the case then ignoring these issues is ultimately a disservice to the potential growth of the architecture.

By choosing to address the issues incumbent in acting in the real world it is also possible to draw insights into their interaction with each other and the effect that increased knowledge (provided by high-level cognitive capabilities) can have on their solutions.

Architectures having this environment include:

Go to a List of Common Environmental Considerations.

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