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While a number of disciplines have empirically investigated self-control (e.g., psychology, cognitive science, and sociology), along with philosophy, they have offered differing (although sometimes overlapping) perspectives. A process-based, mechanistic theory explaining empirical self-control data can help integrate these perspectives. A mechanistic (computational) approach through a computational cognitive architecture where simulations can be performed may unify the interpretations of empirical studies based on various (e.g., implicit-explicit) conflicts as well as utility calculation (e.g., from motivational considerations). Such a framework facilitates simulations that account for human data and capture notions of self-control capacity and control fatigue/reduction, facilitating detailed explanations.

We present a skill learning model Clarion. Different from existing models of high-level skill learning that use a topdown approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Clarion is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a minefield navigation task. A match between the model and human data is found in several respects.

This paper introduces a hybrid model that unifies connectionist, symbolic, and reinforcement learning into an integrated architecture for bottom-up skill learning in reactive sequential decision tasks. The model is designed for an agent to learn continuously from on-going experience in the world, without the use of preconceived concepts and knowledge. Both procedural skills and high-level knowledge are acquired through an agent's experience interacting with the world. Computational experiments with the model in two domains are reported.

This paper proposes a fuzzy evidential model for commonsense causal reasoning. After an analysis of the advantages and limitations of existing accounts of causality, a generalized rule-based model F E L {Fuzzy Evidential Logic) is proposed that takes into account the inexactness and the cumulative evidentiality of commonsense reasoning. It corresponds naturally to a neural (connectionist) network. Detailed analyses are performed regarding how the model handles commonsense causal reasoning.

We investigate connectionist models of rule-based reasoning, and show that while such models usually carry out reasoning in exactly the same way as symbolic systems, they have more to offer in terms of commonsense reasoning. A connectionist architecture for commonsense reasoning, C O N - S Y D E R R , is proposed to account for commonsense reasoning patterns and to remedy the brittleness problem in traditional rule-based systems. A dual representational scheme is devised, utilizing both localist and distributed representations and exploring the synergy resulting from the interaction between the two. C O N S Y D E R R is therefore capable of accounting for many difficult patterns in commonsense reasoning. This work shows that connectionist models of reasoning are not just "implementations" of their symbolic counterparts, but better computational models of commonsense reasoning.

Current connectionist models are oversimplified in terms of the internal mechanisms of individual neurons and the communication between them. Although connectionist models offer significant advantages in certain aspects, this oversimplification leads to the inefficiency of these models in addressing issues in explicit symbolic processing, which is proven to be essential to human intelligence. What we are aiming at is a connectionist architecture which is capable of simple, flexible representations of high level knowledge structures and efficient performance of reasoning based on the data. W e first propose a discrete neural network model which contains state variables for each neuron in which a set of discrete states is explicitly specified instead of a continuous activation function. A technique is developed for representing concepts in this network, which utilizes the connections to define the concepts and represents the concepts in both verbal and compiled forms. The main advantage is that this scheme can handle variable bindings efficiently. A reasoning scheme is developed in the discrete neural network model, which utilizes the inherent parallelism in a neural network model, performing all possible inference steps in parallel, implementable in a fine-grained massively parallel computer.