Bridging the gap between subsymbolic and symbolic techniques: A pragmatic approach

We present a survey of methods for passing from subsymbols to symbols. First, we present a definition of the following notions: Subsymbols, Symbols and Hybrid Systems, as well as a taxonomy of Intelligent Systems. Then, pure Artificial Intelligent (AI) as well as pure Computational Intelligence (CI) techniques for subsymbolic to symbolic mapping are exposed. Next, we present a taxonomy of hybrid systems; according to that there are three categories of hybrid systems: Combined Intelligent Systems, Transformational Intelligent Systems and Coupled Intelligent Systems. Hybrid systems are a very promising alternative to pure AI and pure CI techniques for mapping subsymbols to symbols. Finally, we elaborate on the Hybrid Intelligent Systems as well as on the approach, which we follow in Physta research project, which is a subcategory of Hybrid Systems. Key-Words: Neurosymbolic Integration, Neural Networks, Symbols 1 A Pragmatic Framework A framework for the terminology and categorization of intelligent systems as tools for modeling the brain or as machines that are characterized by an intelligent behaviour has been proposed by Bezdec [7]. The main distinction in Bezdec's categorization is made on the basis of the abstraction level of the processing involved in each intelligent system: symbolic or subsymbolic (numerical). Historically, these two main approaches are represented by Artificial Intelligence (AI) and Neural Networks (NN), respectively. The NN approach together with other subsymbolic techniques (fuzzy logic, genetic algorithms etc) has lately been referred to as Computational Intelligence (CI). Knowledge representation in artificial intelligence systems involves the use of symbols. We will adopt the following definition of symbols: a symbol represents abstract notion as well as relevant properties of that abstract notion. Symbolic processes transform symbol structures into other symbol structures that are relevant in a specific context. Although heuristic and linguistic knowledge used by experts can be the basis on which AI systems are constructed, the symbol grounding problem, i.e. the problem of how the symbols acquire meaning, remains one of the main problems of AI [14]. Roughly speaking, this is the top-down view of the problem of subsymbolic to symbolic mapping. Several methodologies (as fuzzy set theory, probabilistic reasoning etc.) have been used in order to support the conventional AI systems in order to bridge the gap between symbols and subsymbols. For example, fuzzy expert systems or decision trees are using numerical data in order to determine the symbolic knowledge. On the other hand, in computational intelligent systems, knowledge is represented in a more opaque, subconceptional manner. The subsymbolic representation is usually defined by the origin of the information (e.g. sensors or database), rather than by the content of the information. Moreover, in a CI system with multiple streams of information, knowledge is usually stored in a distributed fashion. A main objective of mapping subsymbolic information into a symbolic representation is to find an abstract representation of the symbol or object, which is invariant with respect to various features (e.g. invariant with respect to position and orientation). Moreover, the representation should be such that direct links are possible to properties of the object or symbol and such that the representation can easily be used in symbolic reasoning. This problem can be viewed as the bottom-up case of the subsymbolic to symbolic mapping problem. The main approach of computational intelligence is the connectionist approach. Biologically inspired neural networks that try to model the human brain and artificial neural network models that try to emulate the intelligent behaviour of humans are the basic methods of computational intelligence. Lately, supporting methodologies like fuzzy set theory have been used in order to improve the symbolic representation and processing capabilities of NNs. Although each of the subsymbolic and symbolic framework can specify intelligent systems by itself, the limits of each category are not clear and become increasingly vague. The emergent need for intelligent systems that can work in both the symbolic and the subsymbolic framework leads to the unification of AI and CI approaches. Hence, the perspective that the basic method of CI is the NN approach and that the basic method of AI is the traditional symbolic processing (expert systems, rule-based systems) is mainly due to historical reasons. The main issue in the research on intelligent systems is now how the symbolic description of mental processes, in terms of rules and representations in the province of conventional AI, can be related to a subsymbolic description in terms of brain mechanisms (the top-down approach). The same question is how the subsymbolic processes of neurons, synapses and interconnections of a conventional artificial neural network can be related to the symbolic description of human logic and behaviour (the bottom-up approach). The perspective of PHYSTA is based on the bottom-up approach. One of the principal reasons that neural networks have been considered a useful vehicle for such a development is that there is an existence evidence for the solution of creating such a system (and of course answering the above questions), that of the human being. HYBRID INTELLIGENT SYSTEMS