This paper reports and extends discussions carried out during a workshop held at the Santa Fe Institute in August 1995 by the authors. It treats eight major topics: (i) the importance of carefully examining research on routine, (it) the concept of 'action patterns ' in general and in terms of routine, (Hi) the useful categorization of routines and other recurring patterns, (iv) the research implications of recent cognitive results, (v) the relation of evolution to action patterns, (vi) the contributions of simulation modeling for theory in this area, (vii) examples of various approaches to empirical jj; research that reveal key problems, and (viii) a possible definition of 'routine'. An m extended appendix by Massimo Egidi provides a lexicon of synonyms and opposites ji covering use of the word 'routine ' in such areas as economics, organization theory and z artificial intelligence. 6

Problem solvers often have multiple operators available to them but must select just one to apply. We present three experiments that demonstrate that solvers use at least two sources of information to make operator selections in the building sticks task (BST): information from their past history of using the operators and information from the current context of the problem. Specifically, problem solvers are more likely to use an operator the more successful it has been in the past and the closer it takes the current state to the goal state. These two effects, respectively, represent the learning and performance processes that influence solvers ’ operator selections. A computational model of BST problem solving, developed within the ACT-R theory (Anderson, 1993), provides the unifying framework in which both types of processes can be integrated to predict solvers ’ selection tendencies. � 1996 Academic Press, Inc. Most problems can be approached in multiple ways but solved by only a few. Problem solving can be viewed, then, as finding one of the few paths that leads from a problem’s initial state to its goal state through some space of possible intermediate states (Newell & Simon, 1972). In this framework,

In contrast to current intelligent systems, which must be laboriously programmed for each task they are meant to perform, instructable agents can be taught new tasks and associated knowledge. This thesis presents a general theory of learning from tutorial instruction and its use to produce an instructable agent. Tutorial instruction is a particularly powerful form of instruction, because it allows the instructor to communicate whatever kind of knowledge a student needs at whatever point it is needed. To exploit this broad flexibility, however, a tutorable agent must support a full range of interaction with its instructor to learn a full range of knowledge. Thus, unlike most machine learning tasks, which target deep learning of a single kind of knowledge from a single kind of input, tutorability requires a breadth of learning from a broad range of instructional interactions. The theory of learning from tutorial...

ing away from irrelevant details is a theoretical virtue, but the kinds of abstractions that module geography makes can lead to incorrect inferences from data. That such a possibility exists is clearly demonstrated by the working memory research of Just & Carpenter (1992). Briefly, Just and Carpenter have argued that some garden path effects that were previously interpreted in terms of a syntactically encapsulated module can instead be explained by individual differences in working memory capacity. Such an explanation is not considered in a theoretical framework that systematically ignores the role of memory structures in parsing. This point should be taken regardless of whether one is convinced by the current body of empirical support for this particular model---the fact remains that such an explanation could in principle account for the data, and these alternative explanations are only discovered by developing functionally complete architectures. The next few sections describes what ...

Exemplar, prototype, and connectionist models typically assume that events constitute the basic unit of learning and representation in categorization. In these models, each learning event updates a statistical representation of a category independently of other learning events. An implication is that events involving the same individual affect learning independently and are not integrated into a single structure that represents the individual in an internal model of the world. A series of experiments demonstrates that human subjects track individuals across events, establish representations of them, and use these representations in categorization. These findings are consistent with ‘‘representationalism,’ ’ the view that an internal model of the world constitutes a physical level of representation in the brain, and that the brain does not simply capture the statistical properties of events in an undifferentiated dynamical system. Although categorization is an inherently statistical process that produces generalization, pattern completion, frequency effects, and adaptive learning, it is also an inherently representational process that establishes an internal model of the world. As a result, representational structures evolve in memory to track the histories of individuals, accumulate information about them, and simulate

Software reuse is a key issue in any long term software engineering endeavor, such as the ongoing development of robotics systems. Existing approaches to software reuse involve fixing part of the software landscape as a constant foundation to build upon. Some approaches fix the software architecture, i.e., how data flows through the system, and then allow the user to swap components in and out. Other approaches define a suite of stable components and interfaces which can be mixed, matched, and extended. All of these approaches assume that the key to software reuse is pervasive and long lasting standards. Unfortunately, in a young domain such as mobile robotics both software components and software architectures are items of research, and thus are in constant flux. This dissertation proposes the replacement of global, system-wide, permanent standards with local, transient standards in the form of reconfigurable interfaces. These interfaces are not simply libraries of system ca...

Although virtually any human activity can be viewed as the solving of a problem, throughout the hstory of the study of problem solving, most research has concerned tasks that take minutes or hours to per-form. Typically subjects make many observable actions during this period, and these actions are interpreted as the externally visible part of the solution process. Even if subjects are required to solve problems in their heads (for example, to mentally multiply 135 x 76), they are usually asked to talk aloud as they work, and the resulting verbal protocol is interpreted as a sequence of actions (see chapter 1). Thus the tasks studied are not only long tasks but also multistep tasks. The earliest experimental work on human problem solving was done

The role of action has been strongly emphasized, not only in cognitive research on learning and problem solving, but also in education and instructional psychology. The Constructivism tradition has long asserted that action plays a crucial role for learners in constructing their own knowledge. In an educational context, active engagement entails students examining their own ideas, considering alternative explanations for newly taught concepts, and evaluating competing perspectives. Some theorists (e.g., Anzai & Simon, 1979) propose that these processes are found when learning is by doing. However, a constructivist perspective implies that instructional formats enable self-monitoring (e.g., Covington, 2000; Pintrich & De Groot, 1990), which includes reflective activities such as describing, explaining, and evaluative thinking (e.g., Covington, 2000; Zimmerman, 1990), which are not exclusive to action. The present article discusses findings that concern two related and thus far, unexplored two questions: How affective is observation-based learning in a complex skill learning task that usually requires processes that involve active engagement with it? How does monitoring affect the transfer of problem solving ability in complex skill learning task? The first aim of the article is to introduce ways of using common educational tools like the self-observation technique, which involves re-exposing individuals to their own self-generated behaviors, in novel ways that can provides insight into how people use self-regulatory mechanisms like monitoring on internally represented behaviors. The second aim is provide support for the view that in the absence of active learning, learning indirectly (i.e. Observation-based learning) is a practical and in some cases necessary method of knowledge and skill acquisition, and does not in turn lead to decrements in acquired knowledge and skill. Finally, the article presents the argument that the degree of self-monitoring that takes place may be a mediating factor in preserving the view that action has a special status in knowledge acquisition.

This article may not exactly replicate the final version published in the APA journal. It is not the copy of record. Dynamics and constraints in insight This paper reports two experiments that investigated performance on a novel insight problem, the eight-coin problem. We hypothesized that participants would make certain initial moves (“strategic moves”), that seemed to make progress according to the problem instructions, but that nonetheless would guarantee failure to solve. Experiment 1 manipulated the starting state of the problem and showed that overall solution rates were lower when such strategic moves were available than when they were not available. Experiment 2 showed that failure to capitalize upon salient visual hints about the correct first move was also associated with the availability of strategic moves in the starting state of the problem. The results are interpreted in terms of an information-processing framework previously applied to the nine-dot problem, and generalized here to a new class of insight problems (which includes the present eight-coin problem). We argue that, in addition to the

Don’t teach me 2 + 2 equals 4: Knowledge of arithmetic operations hinders equation learning This study investigated whether children’s knowledge of arithmetic operations hinders their ability to solve novel equations after instruction. Second- and third-grade children completed a timed arithmetic pretest as a means for assessing their proficiency with arithmetic operations. Next, they received lessons on the principle of mathematical equivalence either in a context designed to activate their knowledge of arithmetic operations (e.g., 15 + 13 = 28), or in a context designed to not activate their knowledge of arithmetic operations (e.g., 28 = 28). Then, children completed an equation-solving posttest (e.g., 3 + 9 + 5 = 6 + __). After the posttest, children switched lesson contexts and completed the posttest again. Children solved more equations incorrectly after receiving lessons in the operational context. Additionally, the operational context led children who were most proficient with arithmetic operations to solve more equations using the typical addition strategy of adding up all the numbers. Results highlight that the activation of existing knowledge can interfere with the acquisition of new information. Some domains of knowledge are particularly difficult for people to learn, even after significant amounts of training or instruction. There are many examples of this in our formal education system, including reading, mathematics, science, and foreign language. Over the past several years, a number of scientists (e.g., Flege,