Ling Rothrock

Review and reappraisal of adaptive interfaces: Toward biologically inspired paradigms

The field of Adaptive Interfaces has been an active area of research for over 10 years. While there have been great advances, unresolved issues remain. The paper presents a reappraisal of adaptive interfaces with an eye toward addressing these issues using biologically inspired methods. We first define a general and theoretical model of adaptive interfaces based on a survey of existing research. Using our generalized adaptive interface model, we then proceed to build taxonomies of variables used for adaptation. The aim is to provide researchers, designers and builders a better understanding of the underlying mechanisms, processes and outcomes of adaptive interfaces. From our review, we propose design rules that address three primary elements of a generalized adaptive interface: the identification of variables that call for adaptation, the determination of necessary modifications to the interface, and the selection of the decision inference mechanism. We then turn to the investigation of an alternative method for adaptive interface design. To find a method that corresponds better to human decision-making, which has been characterized as situated and recognition-primed, we explored biologically inspired techniques. In particular, we focus on the correspondence between human decision-making behaviour and the concepts of emergence and self-organization. While our ruminations are speculative, the future of biologically inspired interfaces seems promising.

Agent-based simulation of affordance-based human behaviors in emergency evacuation

Abstract Complex cognitive processes corresponding to human control behaviors cannot be easily inferred using (1) a logical rule-based model, (2) a statistical model, or (3) an analytical predictive model. Predicting human behaviors in complex and uncertain environments like emergency evacuation is considered almost impossible (at least NP hard) in systems theory. In this paper, we explore simulating human behaviors using affordance-based finite state automata (FSA) modeling, based on the ecological concept of affordance theory. To this end, we introduce the conceptual and generic framework of affordance-based human behavior simulation developed through our previous work. Following the generic framework, formal simulation models of affordance-based human behaviors are developed, especially for emergency evacuation, to mimic perception-based dynamic human actions interacting with emergent environmental changes, such as fire. A “warehouse fire evacuation” case is used to demonstrate the applicability of the proposed framework. The human action planning algorithms in the simulation model are developed and implemented using the Adjusted Floor Field Indicators, which represent not only the evacuee’s prior knowledge of the floor layout but the perceivable information about dynamic environmental changes. The results of our simulation study verify that the proposed framework accurately simulates human fire evacuation behavior. The proposed framework is expected to capture the natural manner in which humans behave in emergency evacuation and enhance the simulation fidelity of analyses and predictions of perceptual human behaviors/responses in the systems by incorporating cognitive intent into human behavior simulations.

Integrating Compensatory and Noncompensatory Decision-Making Strategies in Dynamic Task Environments

This article summarizes the ongoing work to analyze compensatory and noncompensatory decision making behaviors using a common framework known as Brunswik’s Lens Model. The authors begin with a survey of existing work in modeling compensatory decision making behavior using the Lens Model and an overview of initial forays into noncompensatory modeling. An example is provided of an instance in which both compensatory and noncompensatory decision making may occur in the same task under different circumstances. Formulations of the Lens Models to account for both types of decisions behaviors are then discussed followed by speculations on a consolidated framework to incorporate both models.

Inferring rule-based strategies in dynamic judgment tasks: toward a noncompensatory formulation of the lens model

Performers in time-stressed, information-rich tasks develop rule-based, simplification strategies to cope with the severe cognitive demands imposed by judgment and decision making. Linear regression modeling, proven useful for describing judgment in a wide range of static tasks, may provide misleading accounts of these heuristics. That approach assumes cue-weighting and cue-integration are well described by compensatory strategies. In contrast, evidence suggests that heuristic strategies in dynamic tasks may instead reflect rule-based, noncompensatory cue usage. We therefore, present a technique called genetics-based policy capturing (GBPC) for inferring noncompensatory rule-based heuristics from judgment data as an alternative to regression. In GBPC, rule-base representation and search uses a genetic algorithm, and fitting the model to data using multiobjective optimization to maximize fit on three dimensions: completeness (all human judgments are represented); specificity (maximal concreteness); and parsimony (no unnecessary rules are used). GBPC is illustrated using data from the highest and lowest scoring participants in a simulated dynamic, combat information center (CIC) task. GBPC inferred rule-bases for these two performers that shed light on both skill and error. We compare the GBPC results with regression-based lens modeling of the same data set, and discuss how the GBPC results allowed us to interpret the high scoring performers highly significant use of unmodeled knowledge (C=1) revealed by lens model analysis. The GBPC findings also allow us to now interpret a similarly high use of unmodeled knowledge (C=1)in a previously published lens model analysis of a different data set collected in the same experimental task. We conclude by discussing training implications, and also prospects for the development of integrated GBPC models of both human judgment and the task environment, thus providing a noncompensatory formulation of the lens model (a genetics-based lens model, or GBLM) of the integrated human-environment system.

A theoretical framework and quantitative architecture to assess team task complexity in dynamic environments

The predominance of team decision-making and performance assessment literature has defined team measures as highly abstract concepts (e.g., team leadership, competence, innovation, empowerment). Likewise, a clear taxonomy for defining team tasks has remained elusive. Thus, this paper presents a framework by which to classify team tasks based on two basic premises: (1) a team task can be broken down into quantifiable components; and, (2) team performance can be used to evaluate a tasks complexity relative to another task. This framework relies on the ability to objectively measure individual team member subtasks relative to a team objective that is composed of several windows of opportunity that must be achieved by individual members to achieve good team performance. This proposed theoretical framework takes a simulation-based approach by which to evaluate team tasks and performance. The approach is driven by the need to understand team tasks and their relative performance in military, government, and commercial applications.

Using the Analytic Hierarchy Process to Examine Judgment Consistency in a Complex Multiattribute Task

This paper investigates the impact of framing and time pressure on human judgment performance in a complex multiattribute judgment task. We focus on the decision process of human participants who must choose between pairwise alternatives in a resource-allocation task. We used the Analytic Hierarchy Process (AHP) to calculate the relative weights of the four alternatives (i.e., C1, C2, C3, and C4) and the judgment consistency. Using the AHP, we examined two sets of hypotheses that address the impact of task conditions on the weight prioritization of choice alternatives and the internal consistency of the judgment behavior under varying task conditions. The experiment simulated the allocation of robotic assets across the battlefield to collect data about an enemy. Participants had to make a judgment about which asset to allocate to a new area by taking into account three criteria related to the likelihood of success. We manipulated the information frame and the nature of the task. We found that, in general, participants gave significantly different weights to the same alternatives under different frames and task conditions. Specifically, in terms of ln-transformed priority weights, participants gave significantly lower weights to C2 and C4 and higher weight to C3 under gain frame than under loss frame, and also, under different task conditions (i.e., Tasks #1, #2, and #3), participants gave significantly higher weight to C4 in Task #1, lower weights to C1 and C4, higher weight to C3 in Task #2, and lower weight to C3 in Task #3. Furthermore, we found that the internal consistency of the decision behavior was worse, first, in the loss frame than the gain frame and, second, under time pressure. Our methodology complements utility-theoretic frameworks by assessing judgment consistency without requiring the use of task-performance outcomes. This work is a step toward establishing a coherence criterion to investigate judgment under naturalistic conditions. The results will be useful for the design of multiattribute interfaces and decision aiding tools for real-time judgments in time-pressured task environments.

A formal control-theoretic model of a human–automation interactive manufacturing system control

This paper describes a human–automation interactive manufacturing system and presents a formal model for describing and controlling the system. The model presented in this paper considers a system from two perspectives: structural and operational perspectives. Human and an automated controller are considered agents that cooperate to achieve given goals by executing assigned tasks. A human–automation interaction is described with a particular communication model between two agents that exchanges messages. A system control schema and human tasks are modelled separately and then integrated in a formal manner using a modified communicating finite state machine framework. An interface model that coordinates the message exchanges between two agents is then introduced. An automated shop floor control system with a human material handler is modelled with the proposed framework and a simple illustrative example is provided.

Using Time Windows to Evaluate Operator Performance

A proper understanding of human performance characteristics is a prerequisite for designers of complex systems. Although human factors texts provide some insights into basic performance issues, the emergence of highly automated computing systems have fundamentally altered the way humans work. The purpose of this article is to present a research approach to quantify and analyze human performance within a complex, time-critical system. The approach is centered on a measurement construct, called a time window, which enables a functional relation between constraints on operator activities and time availability. A blackboard model is developed as the mechanism to generate, maintain, and complete time windows. Moreover, an object-oriented methodology is described that implements the blackboard model within a realistic task context. To demonstrate the utility of time windows, an existing implementation in a real-time human-in-the-loop simulation is also described. Using time window outcomes, some cursory analyse...

Using finite state automata (FSA) for formal modelling of affordances in human-machine cooperative manufacturing systems

Modelling complex systems poses significant challenges on how one represents the system components and interactions among them. In order to provide a systematic approach to represent human participation as a part of a dynamic system, this paper presents a formal automata model of human-machine cooperative systems that incorporates human capabilities with respect to system conditions. Specifically, we propose a control model for human-involved shop floor systems based on discrete event-based systems (DES) and an environmental concept known as an affordance. When modelling human-involved systems where a human operator is considered a crucial system component, it is necessary to analyse the model complexity that increases significantly due to a humans behavioural patterns. From the perspective of the temporal and physical state domains a human operators behaviour is usually limited by attention and resource constraints. We investigate these limitations and map them into constrained system affordances, and then construct a formal human-machine cooperative model based on the finite state automaton (FSA) model. The proposed model can provide a framework to combine human activities into systems operations in consideration of humans effectivities and system affordances. A detailed application example is provided to illustrate that the proposed model can effectively be applied to manufacturing control systems.

Formal model of human material-handling tasks for control of manufacturing systems

To achieve an effective integration framework for manufacturing systems, a formal model of a system is highly desired. In spite of significant work on automated manufacturing systems, human operators still play a critical role in virtually every system, especially for material-handling processes. To build a model for control and analysis of a system where a human operator is integrated, a formal functional specification of a human material handler (MH) is presented in a hierarchical framework. Two types of human operational errors associated with material-handling tasks are also classified and discussed. A shop floor control example is provided to illustrate the proposed modeling framework