BASIC ASSUMPTIONS OF COMPUTATIONAL INTELLIGENCE

debate.

2.4 BASIC ASSUMPTIONS OF COMPUTATIONAL

INTELLIGENCE

Computational intelligence is considered as an empirical inquiry due to its exploratory nature. The complex tasks involved in computational intelligence require us to make reasonable assumptions, and carry out research based on these assumptions. We should notice that different assumptions have been made due to concerns from various perspectives (such as philosophy, psychology, as well as others). Debates have been carried out around these assumptions. In the following, we briefly examine some important assumptions used in computational intelligence (but we will not get involved into these debates).

2.4.1 SYMBOLISM

2.4.1.1 Physical symbolism and representation

This assumption states that intelligent actions are demonstrated based on physical symbols. A symbol is just a token to denote a thing which has a well- defined meaning. For example, "student" is a symbol denoting a concrete thing (an object), "thought" is a simple denoting an abstract object, and "take" is also a symbol denoting an activity. Symbolism serves as the foundation for state space search and knowledge representation, two of the most fundamental issues discussed in artificial intelligence literature.

2.4.1.2 Physically grounded

The physical-ground hypothesis assumes that in order to build a system that is intelligent, it is necessary to have representations grounded in the physical world. This assumption challenges the physical-symbol system hypothesis in computational intelligence. The hope is that the physical-ground hypothesis obviates the need for symbolic representations or models because the world becomes its own best model. This assumption has been adopted by some researchers in computational intelligence, but is not widely accepted. We will not pursue this direction further.

2.4.1.3 Subsymbolism

The basic feature of subsymbolism is to de-emphasize the use of symbols to denote objects and relations; intelligence is viewed as arising from the collective behavior of large numbers of simple, interacting components. A well-known example of subsymbolism is neural networks (NNs). Unlike symbol-based computational intelligence, a neural network system assumes no correspondence between the units of computation and objects or relations in the world. There is a distributed representation: represent knowledge implicitly in patterns of interactions between components (weights). For this reason, the term connectionism has been used to describe neural networks. In this book, although we do not locate any chapter or section to discuss neural networks, from time to time we will compare this approach with other approaches.

Figure 2.7 A simple neural network

2.4.1.4 Other approaches

Other approaches also exist. For example, Copycat's architecture is claimed as neither symbolic nor connectionist, nor as a hybrid of these two (although it can be thought as this way). [Hofstadter 1995] argued that the program has a novel type of architecture somewhere in between these extremes. It is an emergent architecture, in the sense that the program's top-level behavior emerges as a statistical consequence of myriad small computational actions, and concepts in creating analogies can be considered to by realization of "statistically emergent active symbols." Since approaches like this are not popular, we will not pursue this direction further.

2.4.2 SEQUENTIAL OR PARALLEL

The concept of artificial neural network goes beyond subsymbolism. In fact, the distributed nature of neural network (as discussed above) makes it a perfect example of massive parallel processing. Each artificial neuron can be

Output unites, Intepreted as classifications House Plant ❍ ❍ ❍ ❍ wn Adjustable weights wm ❍ ❍ ❍ ❍ ❍ ❍ Hidden layer wk ❍ ❍ ❍ ❍ Input units wj wi (Image)

considered as an extremely simple processing element, and these processing elements can process information in parallel. In this sense, neural networks are at odds with sequential models as exemplified by Newell's "Unified theories of cognition"[Newell 1990].

The UTC presents a cognitive architecture rooted in conceptually serial register-transfer level of computer architecture (so little adapted to the needs of cognitive neuroscience). Newell emphasizes a serial symbol-computation perspective throughout. Most parts of this book will follow this tradition.

2.4.3 LOGIC-BASED APPROACH

An influential viewpoint in traditional computational intelligence community is that computational intelligence urgently needs mathematical and logical theory. As a founder of computational intelligence, John McCarthy [Lifschitz 1991] argued that we will not reach human level intelligence by more algorithms reducing the complexity of a problem from n2 to n log n. The more common sense we formalize, the more we will need to develop logic, exactly as has happened for physics and mathematics. However, the choice of a logic-based approach to computational intelligence has been very controversial. The main problem is that logic has been developed with goals quite different from computational intelligence, e.g., to prove the consistency of mathematical reasoning, or to provide semantics to (parts of) natural language. Although logic is a very good starting point which allows formalizing many forms of common sense, it is far from having the expressibility needed to represent basic notations in computational intelligence. Nevertheless, a logic-based approach provides a standard for studying various useful forms of reasoning. For example, production systems model (to be discussed in Chapter 5) can be considered as a "loose" form of logic, and conceptual graphs (Chapter 6) can be converted into logic by following certain steps. In addition, conceptual and logical data modeling can also benefit from logic (Chapters 4 and 6). Therefore, logic can be considered as the starting point of an integrated approach for decision making, and will be discussed in the next chapter.

2.4.4 HUMAN INTELLIGENCE AS METAPHOR

Computational intelligence is exploratory in nature and is thus an empirical science [Simon 1995]. Since the natural intelligence (particularly, human intelligence) is the only model we are familiar with, it is natural to use human intelligence as the model to develop computational intelligence systems. However, this does not mean computational intelligence must follow the exact ways human beings approach reasoning.

In addition, there may be many different ways to use human intelligence as a metaphor. In fact, symbolism and subsymbolism can be considered as two different ways of modeling intelligence -- at the cognition level or at the brain level. We should note, however, using human intelligence as a metaphor is not the only option. In fact, recent studies in artificial life (AL) and adaptive

behavior have tried to re-situate computational intelligence-related research within the context of an artificial biology and zoology, respectively. The bottom line of these directions is that we need much more understanding of the animal substrates of human behavior before we can fulfil the dreams of computational intelligence [Humphrys 1999].

2.4.5 SUMMARY

In summary, the assumptions used by "mainstream" computational intelligence can be illustrated through Newell's United Theories of Cognition (UTC) framework [Newell 1990], which has three principal themes:

(1) Psychology has arrived at the possibility of unified theories of cognition;

(2) There is a common foundation underlying cognitive science; (3) A n ar ch itectu re called S o ar d ev elo ped b y New ell and h is research group is a candidate unified theory of cognition that is useful as an exemplar of the concepts.

Fundamental knowledge system functions of UTC include the following:

• symbol (as already discussed);

• representation: symbol structures act as representations as they obey. Newell's basic representation law can be expressed as

Decode [Encode[T](Encode[S])] = T(S),

where T stands for transformation while S stands for situation. So this formula says that transforming a situation is done by encoding both the transformation and situation and then decode them.

In the remaining part of this chapter, we will examine the two most important issues of computational intelligence under the UTC framework, namely, search and representation.