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The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling.

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.

Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems.

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.