Unifying declarative knowledge and procedural knowledge with "procedural interpretation", as in Logic programming.
In programming languages, operations correspond to instructions, statements, routines, procedures, functions, etc.
In the definition of an operation, an input argument can be an independent variable, and an output argument is a dependent variable. When executed, all input arguments are instantiated by constants, and the output arguments are determined accordingly.
Knowledge about an operation can either be inheritance/similarity or implication/equivalence. The inference rules are used as usual.
Since NAL does not attempt to completely specify the conditions and consequences of an operation, it does not suffer from the Frame Problem.
Compound operations can be formed as compound events, and executed without much reasoning.
With a given basic operation set, Narsese can be used as a programming language, and reasoning carries out planning, skill learning, self-programming, etc., like a logical programming language with the inference engine as an interpreter.
NARS can also learn to program in another programming language by using Narsese as the meta-language.
The execution of an atomic operation is normally not the duty of the inference engine, but that of the host system (as the "body") or a peripheral device (as a "tool").
Sensorimotor interface: operations are registered in NARS, with initial knowledge.
NARS can serve as a mind of a robot or an intelligent operating system controlling various application software.
NARS+: NARS plus tools/organs. Multiple I/O channels.
Different representations of "goal" in AI: state, utility/fitness/reward function. Problem: a goal is described as certain, static, and complete.
AIKR implies that a system may have conflicting, competing, and changing goals. To handle this situation, a relative "degree of desire" is need.
A desire-value is defined on every event, indicating the extent it implies a (virtual) desired event. Therefore, desire-value is a derivative of truth-value, and can be processed accordingly.
Goal processing in NARS:
The decision-making rule check the plausibility of a highly desired event, and turn it into a goal that is actually pursued. This is where binary decision is needed, in spite of the multi-valued truth-values, desire-values, and priority-values.
Compared with traditional decision theory, such as the expected utility hypothesis: no given probability and utility functions.
Due to AIKR, goal alienation becomes inevitable.
The unification in representation (the term hierarchy) and processing (the inference framework) in NARS: