During the 1980s, the Artificial Intelligence Center at SRI International developed the procedural reasoning system (PRS) concept.
Their framework utilized and popularised the BDI model in software for intelligent agent control. The framework’s seminal application was a fault detection system for the NASA Space Shuttle Discovery’s reaction control system.
Furthermore, the Australian Artificial Intelligence Institute continued to work on this PRS until the late 1990s, resulting in the development of dMARS, a C++ implementation and extension.
Real-time reasoning systems
A procedural reasoning system (PRS) in artificial intelligence is a framework for building real-time reasoning systems capable of handling challenging tasks in dynamic environments. It is the idea of an intelligent agent that uses the belief-desire-intention software model and is rational. A set of knowledge areas is typically defined as a user application and provided to a PRS system.
In contrast to robotic architectures, each knowledge area is a discrete piece of procedural knowledge that specifies how to do something, such as navigating a corridor or planning a path. The agent is a program like this and a PRS interpreter. Furthermore, the interpreter is in charge of upholding assumptions about the current state of the world, selecting the next set of goals to work toward, and deciding which body of knowledge to apply to the given circumstance. These operations may be carried out in a specific way depending on domain-specific meta-level knowledge areas.
Structure
The following elements are part of PRS’s system architecture:
- Database for beliefs about the world, represented using first-order predicate calculus.
- Goals by the system as conditions over an interval of time on internal and external state descriptions (desires).
- Knowledge areas (KAs) or plans define sequences of low-level actions toward achieving a goal in specific situations.
- Intentions include those KAs that have for current and eventual execution.
- Interpreter or inference mechanism that manages the system.
Features
The following lists the general specifications for their PRS’s development:
- asynchronous event handling
- guaranteed reaction and response types
- procedural representation of knowledge
- handling of multiple problems
- reactive and goal-directed behaviour
- focus of attention
- reflective reasoning capabilities
- continuous embedded operation
- handling of incomplete or inaccurate data
- handling of transients
- modelling delayed feedback
- operator control
In contrast to conventional AI planning systems, PRS interleaves planning and carrying out actions worldwide. The system might only have a partially defined plan at any given time.
Belief-desire-intention
The belief-desire-intention (BDI) framework for intelligent agents is the foundation for PRS. A person’s beliefs are what they hold to be true about how the world is right now, while their desires and intentions are what they are doing to work toward those goals. In addition, unlike purely reactive systems like the subsumption architecture, each of these three components is within the PRS agent at runtime.
Conclusion
The PRS from SRI was for embedded use in dynamic and real-time environments. It addressed the shortcomings of other contemporary control and reasoning architectures, such as expert systems and the blackboard system. Furthermore, PRS has tested Shakey’s robot, including navigation. Later applications included a network management monitor for Telecom Australia called the Interactive Real-time Telecommunications Network Management System (IRTNMS).
Source: indiaai.gov.in
