Decision Validation and Emotional Layers on Fuzzy Boolean Networks • José A. B. Tomé João P. Carvalho Instituto Superior Técnico/ INESC-ID Instituto Superior Técnico/ INESC-ID Rua Alves Redol nº9, 1000 Lisboa, Portugal Rua Alves Redol nº9, 1000 Lisboa, Portugal jose.tome@inesc-id.pt joao.carvalho@inesc-id.pt Abstract. Fuzzy Boolean Networks are capable of learning and reasoning. However, the reasoning result must be validated by an emotional layer, which ensures the acting rules are meaningful and that no contradictory rules are giving a wrong, “averaged”, defuzzified result. Here the problem is addressed and an emotional layer is developed to deal with it. I. INTRODUCTION Fuzzy Boolean Nets can learn from real experiments and they are capable to perform qualitative reasoning based on their internal memories, which are set during the learning phase [8]. These nets can be considered a neural fuzzy model where the fuzziness is an inherent emerging property in contrast with other known models where fuzziness is artificially introduced on neural nets or where neural components are inserted on the fuzzy systems [2, 3, 4, 5, 7]. Moreover, it has been proved [9] that they are Universal Approximators (they are equivalent to Parzen Windows estimators [6]) and that they are capable to distinguish between different rules, provided a relationship between the number of rules and the number of inputs per neuron is obeyed [10]. Based on this theoretical background some applications have been implemented [12], but there are interesting questions yet to be developed, such as the one here treated. Since the learnt rules are distributed among a network of neurons, two important questions arise: 1. The first one is: how can the network, itself, know that it has learnt enough from the real world, in order to trust its output on a future application experiment (or, in other words, is the performed reasoning trustable, based on the teaching)? 2. The second question is related with situations where different activated rules provide different outputs from the same input data of a given experiment, in a dangerously way. This situation tends to set the system output on a kind of “average” of the different rule decisions. This problem arises also on the traditional fuzzy systems, where the process of deffuzification gets the “responsibility” of solving it. It is the known case of the automobile control when this “sees” one obstacle ahead, just in front. If some of the rules drive the automobile to the left, in order to avoid the eminent disaster, • This work is partially supported by FCT project POSI/SRI/33741/2000 and some other rules drive it to the right the result may be catastrophic! On Fuzzy Boolean Nets the network is by itself and must decide what to do, without any external, artificial or algorithmic help. It is the purpose of this paper to present a natural (embedded on the network) solution to these problems. The first problem is easily solved if one uses, in addition to the internal activation ratios of the neurons, the non-taught ratio. That is, the output of the system (which is represented by the ratio of “ones” among every output neuron) is provided together with a “credibility” output (also a ratio of activated neurons), which gives an indication about the trust one can put on the output. The second problem is more interesting and is dealt with another layer of neurons, which inputs do not come from the external variables but from the internal states of the reasoning layer of neurons. This is possible because when reasoning takes place, and due to the internal structure of the binary neurons, the network disposes of all necessary information about how every rule is firing, about the output activation ratio and thus about any incoherence. That is, one uses a meta-network that interprets the work of the network itself and validates its decisions. Reasoning may be performed but decisions not taken at all, if the meta- network is not satisfied with the process. In a certain way this can be viewed as a neural network that provides “emotion” to the reasoning process itself. It is known that on animals, humans particularly, real decisions can not be achieved without the work of the emotional layers [1], even if reasoning has been performed. II. EMBEDDING LEARNING KNOWLEDGE The values taken by the linguistic terms on a given experiment are expressed by the binary memories activation ratios and these determine the consequent area activation ratio (which represents the consequent deffuzification). Then, any initial activation ratios (or corresponding to antecedent rule areas not taught enough) have no meaning but wrongly affect the consequent. In order to deal with this situation it is necessary to memorize, for each joint antecedent area of each neuron (corresponding to a given rule antecedent), not only the Boolean value (“0” if that joint antecedent has not been addressed or “1” if it has) but also extra binary information specifying if any thing at all has or not been taught. In hardware terms this would mean two bits of memory per