Connectionist Model in Artificial Intelligence

2009

Because of the big complexity of the world, the ability to deal with uncertain and to infer "almost" true rules is an obligation for intelligent systems. Therefore, the research of solution to emulate Inductive Reasoning is one of the fundamental problem of Artificial Intelligence. Several approaches have been studied: the techniques inherited from the Statistics one side, or techniques based on Logic on the other side. Both of these families show complementary advantages and weakness. For example, statistics techniques, like decision trees or artificial neural networks, are robust against noisy data, and they are able to deal with a large quantity of information. However, they are generally unable to generate complexes rules. On the other side, Logic based techniques, like ILP, are able to express very complex rules, but they cannot deal with large amount of information. This report presents the study and the development of an hybrid induction technique mixing the essence of statistical and logical learning techniques i.e. an Induction technique based on the First Order Logic semantic that generate hypotheses thanks to Artificial Neural Networks learning techniques. The expression power of the hypotheses is the one of the predicate logic, and the learning process is insensitive to noisy data thanks to the artificial neural network based learning process. During the project presented by this report, four new techniques have been studied and implemented: The first learns propositional relationship with an artificial neural network i.e. induction on propositional logic programs. The three other learn first order predicate relationships with artificial neural networks i.e. induction on predicate logic programs. The last of these techniques is the more complete one, and it is based on the knowledge acquired during the development of all the other techniques. The main advance of this technique is the definition of a convention to allow the interaction of predicate logic programs and artificial neural networks, and the construction of Artificial Neural Networks able to learn rule with the predicate logic power of expression.

International Journal of Electrical and Computer Engineering (IJECE), 2018

Modeling higher order cognitive processes like human decision making come in three representational approaches namely symbolic, connectionist and symbolic-connectionist. Many connectionist neural network models are evolved over the decades for optimizing decision making behaviors and their agents are also in place. There had been attempts to implement symbolic structures within connectionist architectures with distributed representations. Our work was aimed at proposing an enhanced connectionist approach of optimizing the decisions within the framework of a symbolic cognitive model. The action selection module of this framework is forefront in evolving intelligent agents through a variety of soft computing models. As a continous effort, a Connectionist Cognitive Model (CCN) had been evolved by bringing a traditional symbolic cognitive process model proposed by LIDA as an inspiration to a feed forward neural network model for optimizing decion making behaviours in intelligent agents. Significanct progress was observed while comparing its performance with other varients.

Cognitive Science, 1982

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectianist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise-sensitivity, distributed decisionmaking, time and sequence problems, and the representation of complex concepts.

Behavioral and Brain Sciences, 1990

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and "simple" homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities (including "distributed representations") or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks.

Artificial Neural Networks -- Comparison of 3 Connectionist Models

Behavioral and Brain Sciences, 1988

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models.Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule appli...

Artificial Intelligence is the study of how to make machines behave in an intelligent manner, such as processing information, solving complex problems, or simply providing entertainment. Digital computers are general symbol manipulators capable of quickly applying strict rules to large collections of symbols, to transform these symbols in a way meaningful and useful to people. This sort of "rule-based computation" is one popular model in AI of how machines (including the brain) can behave intelligently. Another model called "connectionism" is based on the interaction of simple processing units combined in a large network, resembling the network of neurons in the human brain. The connectionist approach to intelligence has many features which may make it more appealing as an model of intelligent systems than rule-based computation.

2013

Connectionist network is the network of processing elements. These processing elements are connected with each other. It suggests that we may consider it as the fully connected network. The processing elements of this network are basically MP model neuron and normally, employee the bipolar non-linear output function. This network can be used as the associative memory if some constraints are imposed. The constraints of symmetric interconnections between the nodes and bipolar information processing are the normally used constraints. The associative memory feature of connectionist network has various applications in real world. The most widely used application is the optimization.

Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences, 2020

The development of connectionism represents a paradigm shift in science. Connectionism has its root in cognitive and computational neuroscience. Likening the brain to a computer, connectionism tries to describe human mental abilities in terms of artificial neural networks. A neural network consists of a large number of nodes and units which are joined together to form an interconnection network. Within these interconnections, knowledge is distributed. Therefore learning is a processing by-product. This article is about the concept of connectionism, what it accounts for and what it doesn't take into account. Finally, different approaches to connectionism are discussed.

Cognitive Systems Research, 2002