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This article provides an overview of the EPIC architecture being developed by Kieras and Meyer for modeling human cognition and performance (Kieras, Wood, & Meyer, 1997; Meyer & Kieras, 1997a, 1997b). EPIC is similar in spirit to the Model Human Processor (MHP; Card, Moran, & Newell, 1983), but EPIC incorporates many recent theoretical and empiri- cal results about human performance in the form of a software framework for computer simulation modeling. Using EPIC, a model can be con- structed that represents the general procedures required to perform a complex multimodal task a a set of production rules. When the model is supplied with the external stimuli for a specffic task, it will then execute the procedures in whatever way the task requires, thus simulating a human 's performing the task and generating the predicted actions in simulated real time. EPIC is an architecture for constructing models of 394 performance. It is notet a learning system and so has no mechanisms for learning how to perform a task. Rather, the purpose of EPIC is to repre- sent in detail the perceptual, motor, and cognitive constraints on the human ability to perform tasks

This paper reviews key concepts of the Autonomous Robot Architecture (AuRA). Its structure, strengths, and roots in biology are presented. AuRA is a hybrid deliberative/reactive robotic architecture that has been developed and refined over the past decade. In this article, particular focus is placed on the reactive behavioral component of this hybrid architecture. Various real world robots that have been implemented using this architectural paradigm are discussed, including a case study of a multiagent robotic team that competed and won the 1994 AAAI Mobile Robot Competition. 1 Introduction The Autonomous Robot Architecture (AuRA) was developed in the mid-1980's as a hybrid approach to robotic navigation [6]. Hybridization arises from the presence of two distinct components: a deliberative or hierarchical planner, based on traditional artificial intelligence techniques; and a reactive controller, based upon schema theory [2]. It was the first robot navigational system to be presented ...

A connectionist model of human short-term memory is presented that extends the 'phonological loop' (A.D. Baddeley, 1986) to encompass serial order and learning. Psychological and neuropsychological data motivate separate layers of lexical, timing and input and output phonemic information. Connection weights between layers show Hebbian learning and decay over short and long time scales. At recall, the timing signal is rerun, phonemic information feeds back from output to input and lexical nodes compete to be selected. The selected node then receives decaying inhibition. The model provides an explanatory mechanism for the phonological loop, and for the effects of serial position, presentation modality, lexicality, grouping and Hebb repetition. It makes new psychological and neuropsychological predictions and is a starting point for understanding the role of the phonological loop in vocabulary acquisition and for interpreting data from functional neuroimaging.

All intelligence relies on search --- for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dissertation

The frontal cortex and basal ganglia interact via a relatively well-understood and elaborate system of interconnections. In the context of motor function, these interconnections can be understood as disinhibiting or "releasing the brakes" on frontal motor action plans --- the basal ganglia detect appropriate contexts for performing motor actions, and enable the frontal cortex to execute such actions at the appropriate time. We build on this idea in the domain of working memory through the use of computational neural network models of this circuit. In our model, the frontal cortex exhibits robust active maintenance, while the basal ganglia contribute a selective, dynamic gating function that enables frontal memory representations to be rapidly updated in a task-relevant manner. We apply the model to a novel version of the continuous performance task (CPT) that requires subroutine-like selective working memory updating, and compare and contrast our model with other existing models and th...

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The control of routine action is a complex process subject both to minor lapses in normals and to more severe breakdown followingcertain forms of neurological damage. A number of recent empirical studies (e.g. Humphreys & Ford, 1998; Schwartz et al., 1991, 1995, 1998) have examined the details of breakdown in certain classes of patient, and attempted to relate the findings to existing psychological theory. This paper complements those studies by presenting a computational model of the selection of routine actions based on competitive activation within a hierarchically organised network of action schemas (cf. Norman & Shallice, 1980, 1986). Simulations are reported which demonstrate that the model is capable of organised sequential action selection in a complex naturalistic domain. It is further demonstrated that, after lesioning, the model exhibits behaviour qualitatively equivalent to that observed by Schwartz et al., in their action disorganisation syndrome patients.

Future research on cognitive control must precisely characterize the supervisory functions of executive mental processes. The achievement of this objective will be facilitated by formal concepts and algorithms from contemporary computer operating systems. In particular, operating-system fundamentals can help to advance work with the Executive-Process Interactive Control (EPIC) architecture, a theoretical framework for computational modeling of human multiple-task performance. EPIC models that incorporate general executive processes like those of operating systems provide insights about how people schedule tasks, allocate perceptual-motor resources, and coordinate task processes during multiple-task performance under both laboratory and real-world conditions. Such insights may lead to discoveries about the acquisition of procedural task knowledge and efficient multitasking skills.

This article describes the Adaptive Control of Thought–Rational (ACT–R) cognitive architecture (Anderson et al., 2004; Anderson & Lebiere, 1998) and its detailed application to the learning of algebraic symbol manipulation. The theory is applied to modeling the data from a study by Qin, Anderson, Silk, Stenger, & Carter (2004) in which children learn to solve linear equations and perfect their skills over a 6-day period. Functional MRI data show that: (a) a motor region tracks the output of equation solutions, (b) a prefrontal region tracks the retrieval of declarative information, (c) a parietal region tracks the transformation of mental representations of the equation, (d) an anterior cingulate region tracks the setting of goal information to control the information flow, and (e) a caudate region tracks the firing of productions in the ACT–R model. The article concludes with an architectural comparison of the competence children display in this task and the competence that monkeys have shown in tasks that require manipulations of sequences of elements.