Procedural Reasoning System
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The Procedural Reasoning System (PRS) is a popular agent architecture in artificial intelligence systems. PRS could, for example, be used as a framework when writing a controller for a mobile robot. Some of PRS's features are:
- The "program" that is provided to a PRS system is a set of "knowledge areas". Each knowledge area is a piece of procedural knowledge that specifies how to do something, e.g., how to navigate down a corridor, or how to plan a path. This is in contrast to architectures where the programmer just provides a model of what the states of the world are and how the agent's primitive actions affect them.
- Such a program, together with the PRS interpreter, is used to control the agent. The interpreter is responsible for maintaining beliefs about the world state, choosing which goals to attempt to achieve next, and choosing which knowledge area to apply in the current situation. How exactly these operations are performed might depend on domain-specific meta-level knowledge areas.
- Unlike traditional AI planning systems that generate a complete plan at the beginning, and replan if unexpected things happen, PRS interleaves planning and doing actions in the world. At any point, the system might only have a partially specified plan for the future.
- PRS is based on the BDI or belief-desire-intention framework for intelligent agents. Beliefs consist of what the agent believes to be true about the current state of the world, desires consist of the agent's goals, and intentions consist of the agent's current plans for achieving those goals. Furthermore, each of these three components is typically explicitly represented somewhere within the memory of the PRS agent at runtime, which is in contrast to purely reactive systems, such as the subsumption architecture.
[edit] Architecture Diagram
[edit] References
F. Ingrand, M. Georgeff, and A Rao. An architecture for real-time reasoning and system control. IEEE Expert, 7(6), 1992
