76 4 Brief Survey of Cognitive Architectures

76 4 Brief Survey of Cognitive Architectures 4.4.1.1 Neural-Symbolic Integration The distinction between neural and symbolic systems has gotten fuzzier and fuzzier in recent years, with developments such as e Logic-based systems being used to control embodied agents (hence using logical terms to deal with data that is apparently perception or actuation-oriented in nature, rather than being symbolic in the semiotic sense), see [SSO3a] and [GMIT08]. e Hybrid systems combining neural net and logical parts, or using logical or neural net com- ponents interchangeably in the same role [L Aon]. e Neural net systems being used for strongly symbolic tasks such as automated grammar learning ([Elm91], [Elm91], plus more recent work.) Figure 4.7 presents a schematic diagram of a generic neural-symbolic system, generalizing from [BI05], a paper that gives an elegant categorization of neural-symbolic AI systems. Figure 4.8 depicts several broad categories of neural-symbolic architecture. : “ F “ Interaction Interaction F Representation | ; Symbolic > Neural - Learning (Localist) (Globalist) ) Learning ~ System ‘ System Zz Fig. 4.7: Generic neural-symbolic architecture Bader and Hitzler categorize neural-symbolic systems according to three orthogonal axes: interrelation, language and usage. “Language” refers to the type of language used in the symbolic component, which may be logical, automata-based, formal grammar-based, etc. “Usage” refers to the purpose to which the neural-symbolic interrelation is put. We tend to use “learning” as an encompassing term for all forms of ongoing knowledge-creation, whereas Bader and Hitzler distinguish learning from reasoning. Of Bader and Hitzler’s three axes the one that interests us most here is “interrelation”, which refers to the way the neural and symbolic components of the architecture intersect with each other. They distinguish “hybrid” architectures which contain separate but equal, interacting neural and symbolic components; versus “integrative” architectures in which the symbolic com- ponent essentially rides piggyback on the neural component, extracting information from it and helping it carry out its learning, but playing a clearly derived and secondary role. We prefer Sun’s (2001) term “monolithic” to Bader and Hitzler’s “integrative” to describe this type of system, as the latter term seems best preserved in its broader meaning. HOUSE_OVERSIGHT_012992