We present an analysis of an expert performing a highly interactive computer task. The analysis uses GOMS models, specifying the Goals, Operators, Methods, and Selection rules used by the expert. Two models are presented, one with function-level operators which perform high-level functions in the domain, and one with keystroke-level operators which describe hand movements. For a segment of behaviour in which the expert accomplished about 30 functions in about 30 s, the function-level model predicted the observed behaviour well, while the keystroke-level model predicted only about half of the observed hand movements. These results, including the discrepancy between the models, are discussed.

A system called Merle-Soar is described which demonstrates how a specific architecture for general intelligence and learning (Soar) can be used to reduce scheduling effort when solving simple scheduling problems. In particular, we describe how Merle-Soar schedules sequences of jobs on a single bottleneck machine in a job shop. The knowledge of dispatching, acquired from examining how a human expert performs the task, is cast as search rules. A study was conducted which examined the extent to which learning could contribute to decreases in scheduling effort; specifically, the contribution of learning within-tasks was explored—the change in reasoning effort while solving a particular scheduling problem as knowledge is accumulated from successive trials. The results indicated that dramatic reductions in scheduling effort (in terms of the Soar architecture) were obtained. Knowledge gained early in the scheduling task was subsequently applied later in the task to reduce deliberation, and knowledge gained from one trial successfully reduced deliberation effort in subsequent trials. Additionally, the reduction exhibited the general power law of learning documented in psychological studies of skill acquisition. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Large production systems (rule-based systems) continue to suffer from extremely slow execution which limits their utility in practical applications as well as in research settings. Most investigations in speeding up these systems have focused on match (or knowledge-search) parallelism. Although good speed-ups have been achieved in this process, these investigations have revealed the limitations on the total speed-up available from this source. This limited speed-up is insufficient to alleviate the problem of slow execution in large-scale production system implementations. Such large-scale systems are expected to increase as researchers develop increasingly more competent production systems.

In keeping with our claim that an applied psychology of HCI must be based on cumulative work within a unified framework, we present two extensions of the Model Human Processor. A model of immediate response behavior and stimulus-response (S-R) compatibility is presented and extended to a new domain: transcription typing. Parameters are estimated using one S-R compatibility experiment, used to make a priori predictions in four other S-R compatibility tasks, and then carried over into the area of typing. A model of expert transcription typing is described and its prediction of typing phenomena is demonstrated and summarized.

Rule-based systems are widely used in artificial intelligence for modeling intelligent behavior and building expert systems. Most rule-based programs, however, are extremely computation intensive and run quite slowly. The slow speed of execution has prohibited the use of rule-based systems in domains requiring high performance and real-time response. In this paper we explore various methods for speeding up the execution of rule-based systems. In particular, we examine the role of parallelism in the high-speed execution of rule-based systems and study the architectural issues in the design of computers for rule-based systems. Our results show that contrary to initial expectations, the speed-up that can be obtained from parallelism is quite limited, only about tenfold. The reasons for the small speed-up are: (1) the small number of rules relevant to each change to data memory; (2) the large variation in the processing requirements of relevant rules; and (3) the small number of changes made to data memory between synchronization steps. Furthermore, we observe that to obtain this limited factor of tenfold speed-up, it is necessary to exploit parallelism at a very fine granularity. We propose that a suitable architecture to exploit such fine-grain parallelism is a shared-memory multiprocessor with 32-64 processors. Using such a multiprocessor, it is possible to obtain execution speeds of about 3800 rule-firings/set. This speed is significantly higher than that obtained by other proposed parallel implementations of rule-based systems.

A GOMS theory of stimulus-response compatibility is shown to predict response-time performance on a command/abbreviation encoding task. Working with parameters that were set by an earlier study and which have rational, task-meaningful interpretations as mapping, motor, perception and retrieval operators, zero-parameter predictions were made that fit the observed performance with r2 = 0.776 (p<0.05). The reasonableness of the parameters, the algorithms used to generate the predictions, and the weighting assumption used to combine algorithms into a single prediction are discussed.

Rule-based systems, on the surface, appear to be capable of exploiting large amounts of parallelism—it is possible to match each rule to the data memory in parallel. In practice, however, we show that the speed-up from parallelism is quite limited, less than 10-fold. The reasons for the small speed-up are: (1) the small number of rules relevant to each change to data memory; (2) the large variation in the processing required by the relevant rules; and (3) the small number of changes made to data memory between synchronization steps. Furthermore, we observe that to obtain this limited factor of 10-fold speed-up, it is necessary to exploit parallelism at a very fine granularity. We propose that a suitable architecture to exploit such fine-grain parallelism is a bus-based shared-memory multiprocessor with 32-64 processors. Using such a multiprocessor (with individual processors working at 2 MIPS), it is possible to obtain execution speeds of about 3800 rule-firings/sec. This speed is significantly higher than that obtained by other proposed parallel implementations of rule-based systems.

Carroll and Campbell have exercised themselves over a straw man not subscribed to by us. In the process, our position has not been accurately represented. In reply, we restate as clearly as we can the position for which we actually did and do argue. The underlying issue seems to concern the advantages of using technical psychological theories to identify underlying mechanisms in human-computer interaction. We argue that such theories are an important part of a science of human-computer interaction. We argue further that technical theories must be considered in the context of the uses to which they are put. The use of a theory helps determine what is a good approximation, the degree of formalization that is justified, and the appropriate commingling of qualitative and quantitative techniques. Technical theories encourage cumulative progress by abetting the classical scientific heuristic of divide and conquer.

This paper discusses the prospects of psychology playing a significant role in the progress of human-computer interaction. In any field, hard science (science that is mathematical or otherwise technical) has a tendency to drive out softer sciences, even if the softer sciences have important contributions to make. It is possible that, as computer science and artificial intelligence contributions to human-computer interaction mature, this could happen to psychology. It is suggested that this trend might be prevented by hardening the applicable psychological science. This approach, however, has been criticized on the grounds that the resulting body of knowledge would be too low level, too limited in scope, too late to affect computer technology, and too difficult to apply. The prospects for overcoming each of these obstacles are analyzed here.

This paper presents an experiment in knowledge-intensive programming within a general problem-solving production-system architecture called Soar. In Soar, knowledge is encoded within a set of problem spaces, which yields a system capable of reasoning from first principles. Expertise consists of additional rules that guide complex problem-space searches and substitute for expensive problem-space operators. The resulting system uses both knowledge and search when relevant. Expertise knowledge is acquired either by having it programmed, or by a chunking mechanism that automatically learns new rules reflecting the results implicit in the knowledge of the problem spaces. The approach is demonstrated on the computer-system configuration task, the task performed by the expert system R1.

PAS-II, a computer program which represents a generalized version of an automatic protocol system (PAS-I) is described. PAS-II is a task-free, interactive, modular data analysis sis system for inferring the information processes used by a human from his verbal behavior while solving a problem. The output of the program is a problem behavior graph: a description of the subject's changing knowledge state during problem solving. As an example of system operation the PAS-II analysis of a short cryptarithmetic protocol is presented.

In this paper we describe a complex information processing system, which we call the logic theory machine, that is capable of discovering proofs for theorems in symbolic logic. This system, in contrast to the systematic algorithms that are ordinarily employed in computation, relies heavily on heuristic methods similar to those that have been observed in . human problem solving activity. The specification is written in a formal language, of the nature of a pseudo-code, that is suitable for coding for digital computers. However, the present paper is concerned exclusively with specification of the system, and not with its realization in a computer. The logic theory machine is part of a program of research to understand complex information processing systems by specifying and synthesizing a substantial variety of such systems for empirical study.