**2.1 Defining intelligent systems**

Like many topics, there are wide and narrow definitions of intelligent systems. As the AI domain has evolved, it has fragmented into a variety of subfields which may or may neither be useful for those approaching intelligent systems for the first time nor necessarily informing a general definition. Nonetheless, a starting point is needed. For the purposes of this chapter, we define intelligent systems as systems with the ability to:

"…act appropriately in an uncertain environment, where appropriate action is that which increases the probability of success, and success is the achievement of behavioral subgoals that support the system's ultimate goal." ([15], p. 8).

## **2.2 Toward a general intelligent system architecture**

Langley [16] notes that the fragmentation of the AI domain has led to the proliferation of three primary architectural paradigms—multiagent systems, blackboard systems, and cognitive architectures. **Table 1** summarizes the main characteristics of these architectural paradigms.

For the purposes of this chapter, we focus on Albus and Meystel's [15] reference architecture for intelligent systems, known as the real-time control system (RCS) architecture. RCS has evolved from a robot control schema to one for intelligent system design and has continued to expand while maintaining the validity of its core


**55**

**Table 2.**

*Toward the Intelligent Internet of Everything: Observations on Multidisciplinary Challenges…*

design principles. In its current iteration, 4D/RCS, it is most correctly a cognitive architecture, but as a conceptual architecture for intelligent systems, it accommodates multiple paradigms and approaches to intelligent system design including Dickmanns 4-D approach, behaviorist architectures, and others [17]. Again, while some literature differentiates between intelligent systems and cognitive architectures based on their capacity to evolve through development and use of knowledge to perform new tasks [18], Kotseruba and Tsotsos [14] note others are not as prescriptive. RCS comprises four functional elements, or basic types, of processing module (behavior generation, sensory perception, world modeling, and value judgment),

Together, these modules are implemented in a single architecture with a communication system conveying messages between the various modules and the database module. The level of intelligence in the system is determined by the (i) computational power of the system, (ii) the sophistication of algorithms for behavior generation, sensory perception, value judgment, world modeling, and global communication, (iii) the information and values the system has stored in its memory, and (iv) the sophistication of the processes of the system functioning [15].

For Albus and Meystel, internal and external complexity is managed through hierarchical layering and focused attention, respectively. Recognizing that intelligent systems, in themselves, are extremely complex, they assume a hierarchical control system where higher level nodes are more strategic with longer time horizons and as such less concerned with detail, whereas lower level nodes have a narrower, more

The planning and control of action designed to achieve behavioral goals

Agent A set of computational elements that plan and control the execution of jobs, correcting for

a. the computation of cost, risk, and benefit of actions and plans

(iv) *rules and equations*, (v) *images*, (vi) *maps*, and (vii) task *knowledge*

particularly noteworthy entities observed in current memory input Node A part of a control system that processes sensory information, maintains a world model,

The transformation of data from sensors into meaningful and useful representations of the

a. Uses sensory input to construct, update, and maintain a knowledge database b. Answers queries from behavior generation regarding the state of the world

d. Generates sensory expectations based on knowledge in the knowledge database

b. the estimation of the importance and value of objects, events, and situations

d. the calculation of reward or punishment resulting from perceived states and events

A set of data structures filled with the static and dynamic information that provide a best estimate of the state of the world and the processes and relationships that effect events in the

The knowledge database contains (i) *state variables*, (ii) *entity frames*, (iii) *event frames*,

The knowledge database has both *long* (static or slowly varying) and *short* (dynamic) *memory Entities-of-attention* are entities that have either been specified by the current task or are

errors, and perturbations along the way

A process that performs four principal functions:

c. Simulates results of possible future plans

c. the assessment of reliability of information

computes values, and generates behavior

supported by a knowledge database module as presented in **Table 2**.

*DOI: http://dx.doi.org/10.5772/intechopen.83691*

**Concept Definitions**

world

world

*Selected definitions for intelligent systems [15].*

Behavior definition

Sensory perception

World modeling

Value judgment

Knowledge database

**Table 1.** *Architectural paradigms in AI [16].*

## *Toward the Intelligent Internet of Everything: Observations on Multidisciplinary Challenges… DOI: http://dx.doi.org/10.5772/intechopen.83691*

design principles. In its current iteration, 4D/RCS, it is most correctly a cognitive architecture, but as a conceptual architecture for intelligent systems, it accommodates multiple paradigms and approaches to intelligent system design including Dickmanns 4-D approach, behaviorist architectures, and others [17]. Again, while some literature differentiates between intelligent systems and cognitive architectures based on their capacity to evolve through development and use of knowledge to perform new tasks [18], Kotseruba and Tsotsos [14] note others are not as prescriptive.

RCS comprises four functional elements, or basic types, of processing module (behavior generation, sensory perception, world modeling, and value judgment), supported by a knowledge database module as presented in **Table 2**.

Together, these modules are implemented in a single architecture with a communication system conveying messages between the various modules and the database module. The level of intelligence in the system is determined by the (i) computational power of the system, (ii) the sophistication of algorithms for behavior generation, sensory perception, value judgment, world modeling, and global communication, (iii) the information and values the system has stored in its memory, and (iv) the sophistication of the processes of the system functioning [15].

For Albus and Meystel, internal and external complexity is managed through hierarchical layering and focused attention, respectively. Recognizing that intelligent systems, in themselves, are extremely complex, they assume a hierarchical control system where higher level nodes are more strategic with longer time horizons and as such less concerned with detail, whereas lower level nodes have a narrower, more

