Temporal relations are defined relatively between events. There are two basic temporal relation: "before–after" and "when". NAL allows different granularity and accuracy in representation.
These temporal relations are combined with logical connectors and copulas: two versions of conjunctions and 3 versions of implication (equivalence).
Event-related terms and concepts have time-dependent meaning.
Beside to "reason about time", the system also needs to "reason in time". NARS gets a "personal sense of time" using its internal working cycle as a clock.
The "real-time experience" of NARS measures the time interval between perceived events.
When an event is compared to "now", its temporal feature is represented as its tense. Problem: "now" is a moving reference. Tense is used only in external communication, not in internal representation. Within the system, a time-stamp is attached to each event to remember its occurrence time.
Multiple ways to represent temporal information: (1) built-in temporal relations, (2) internal clock and tense, (3) acquired temporal concepts.
Temporal inference is used to explain the past and to predict the future. In particular, temporal induction can be used to carry out Pavlovian conditioning.
Causal relation is handled in NARS as an acquired notion with domain dependency. Similarly, there are also different types of explanation.
Eternalization is another form of induction where a judgment about a specific moment is taken as evidence for the eternal (time-free) version of the judgment.
In the description 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.
This treatment uniformly represent declarative knowledge and procedural knowledge, as in logic programming.
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 between steps.
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").
The implementation of NAL-8 includes a sensorimotor interface to allow external operations to be registered in NARS, with initial knowledge. In this way, NARS can serve as a mind of a robot, or an intelligent operating system controlling various application software.
A system with a NARS core plus certain special tools or organs is called a "NARS+". Such a system may have multiple I/O channels.
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 desire-value of an event and other factors, and may 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.
The decision making model of NARS is very different from the traditional models, such as decision theory and reinforcement learning.
Not all internal operations of NARS are handled in this way. The selection is based on factors like flexibility, efficiency, and stability.
Now there are two control mechanisms in NARS, an autonomic one and a voluntary one.
Self-awareness and self-control are also restricted by AIKR.
The perceived stream of (external and internal) experience forms the system's consciousness, which is usually subjective, discontinuous, incomplete, and not fully communicable.
The concept of "self" is learned as other concepts, starting from the operations that relate the system to other things.
The appraisal of a statement is represented by its desire-value. It is determined by the statement's logical relation with the goals that have been taken into account.
The overall status of the system in goal satisfaction is indicated by a global satisfaction measurement, which is an accumulation of the satisfaction levels of the recent tasks. This status can be detected and reported by a "feeling" operation.
Each time a concept fires, its desire-value is adjusted according to the current overall feeling of the system. Consequently, the system's feeling about a concept (term) may be various degrees of "like" or "dislike", as well as "mixed" or "don't care".
The system's overall feeling and specific feeling on the concepts are used in decision making and attention allocation to ensure the system to make quick responses to crucial situations under AIKR.