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Article Free Access Share on Panel on affect and emotion in the user interface Authors: Barbara Hayes-Roth Stanford University, Gates Computer Science Building, Stanford, CA Stanford University, Gates Computer Science Building, Stanford, CAView Profile , Gene Ball Microsoft Research, One Microsoft Way, Redmond, WA Microsoft Research, One Microsoft Way, Redmond, WAView Profile , Christine Lisetti Stanford University, Department of Computer Science and Department of Psychology, Stanford, CA Stanford University, Department of Computer Science and Department of Psychology, Stanford, CAView Profile , Rosalind W. Picard MIT Media Laboratory, E15-392, 20 Ames Street, Cambridge, MA MIT Media Laboratory, E15-392, 20 Ames Street, Cambridge, MAView Profile , Andrew Stern PF. Magic, 501 2nd St., Suite 400, San Francisco, CA PF. Magic, 501 2nd St., Suite 400, San Francisco, CAView Profile Authors Info & Claims IUI '98: Proceedings of the 3rd international conference on Intelligent user interfacesJanuary 1998 Pages 91–94https://doi.org/10.1145/268389.268406Online:01 January 1998Publication History 16citation1,051DownloadsMetricsTotal Citations16Total Downloads1,051Last 12 Months29Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper presents the implementation of Tigrito, an affective computer character.We outline how Tigrito can be used to study chidren's sense of engagement and relationship with virtual toys in different modes of interaction.

tFPR is a hybrid fuzzy and structural pattern recognition system that uses fuzzy sets to represent multi-variate pattern classes that can be either static or dynamic depending on time or some other parameter space. The membership functions of the fuzzy sets that represent pattern classes are modeled by sigmoidal functions. The choice of sigmoidal functions was motivated by their ability to represent efficiently and concisely different multi-variate pattern classes. Given a set of input data and a pattern class specification, that is defined as a fuzzy set, tFPR estimates how closely the data match the pattern class specification using the grade of membership of the data in the fuzzy set. The input data may be a number of time-dependent signals whose past values may influence the evaluation of the pattern class. In that case, structural pattern recognition methods, in addition to fuzzy ones, are employed in order to match multi-dimensional curves (rather than points) in the input domain. Two different structural (segmentation) methods are presented. The first is an efficient greedy algorithm that is linear to the number of input data points while the second is an algorithm that is optimal under certain conditions but is slower than the first method. Although efficiency is a very important consideration in tFPR, the main issues are knowledge acquisition and knowledge representation (in terms of pattern classes). tFPR has been implemented in the BB1 blackboard architecture. It is currently being applied in a system for medical monitoring. Potential applications in other domains are under investigation.

This paper describes an informal but systematic method for how to test and verify a knowledge-based system in a large open-ended medical target domain. The system used is Guardian, an intelligent system for monitoring and diagnosis of post-cardiac surgery patients in an intensive-care unit. The knowledge base is tested and verified by running the system on a series of realistic test scenarios, both with an embedded simulator and with an external simulation system. The same scenarios are presented to human test subjects, making it possible to compare and analyze the performance of the knowledge-based system with that of human physicians. The use of simulators instead of clinical data also means that it is possible to test crucial scenarios which occur seldom in medical practice. Our results show that a system like Guardian might indeed be useful in medical care.

Describes the use of explicit models of goals and functions for monitoring and diagnosis of intensive-care patients. The method is based on multilevel flow models (MFM) and used in the Guardian system. It provides this system with alarm analysis, fault diagnosis, and automatic generation of explanations. Advantages include a relatively easy knowledge engineering effort and good properties for use in a system with hard real time deadlines. The results of some experiments are also reported.

An intelligent agent diagnoses perceived problems so that it can respond to them appropriately. Basically, the agent performs a series of tests whose results discriminate among competing hypotheses. Given a specific diagnosis, the agent performs the associated action. Using the traditional information-theoretic heuristic to order diagnostic tests in a decision tree, the agent can maximize the information obtained from each successive test and thereby minimize the average time (number of tests) required to complete a diagnosis and perform the appropriate action. However, in real-time domains, even the optimal sequence of tests cannot always be performed in the time available. Nonetheless, the agent must respond. For agents operating in real-time domains, we propose an alternative action-based approach in which: (a) each node in the diagnosis tree is augmented to include an ordered set of actions, each of which has positive utility for all of its children in the tree; and (b) the tree is structured to maximize the expected utility of the action available at each node. Upon perceiving a problem, the agent works its way through the tree, performing tests that discriminate among successively smaller subsets of potential faults. When a deadline occurs, the agent performs the best available action associated with the most specific node it has reached so far. Although the action-based approach does not minimize the time required to complete a specific diagnosis, it provides positive utility responses, with step-wise improvements in expected utility, throughout the diagnosis process. We present theoretical and empirical results contrasting the advantages and disadvantages of the information-theoretic and action-based approaches.

A domain-specific software architecture (DSSA) is presented that supports development of a variety of intelligent patient monitoring (IPM) agents through component reuse and reconfiguration. Specifically, the IPM DSSA comprises: (a) a reference architecture that supports the shared functional requirements of IPM agents and provides a framework for configuring diverse application-specific sets of components; (b) principles for decomposing IPM expertise into highly reusable components, along with a growing library of such components; and (c) an interactive applications configuration tool that helps a user to select application-relevant components from the library and automatically configures selected components within the architecture. The efficacy of the IPM DSSA is demonstrated with results from the Guardian project, a series of experimental agents for monitoring intensivecare patients.

Our goal is to understand and build comprehensive agents that function effectively in challenging niches. In particular, we identify a class of niches to be occupied by “adaptive intelligent systems (AISs)”. In contrast with niches occupied by typical AI agents, AIS niches present situations that vary dynamically along several key dimensions: different combinations of required tasks, different configurations of available resources, contextual conditions ranging from benign to stressful, and different performance criteria. We present a small class hierarchy of AIS niches that exhibit these dimensions of variability and describe a particular AIS niche, ICU (intensive care unit) patient monitoring, which we use for illustration throughout the paper. To function effectively throughout the range of situations presented by an AIS niche, an agent must be highly adaptive. In contrast with the rather Stereotypie behavior of typical AI agents, an AIS must adapt several key aspects of its behavior to its dynamic situation: its perceptual strategy, its control mode, its choices of reasoning tasks to perform, its choices of reasoning methods for performing chosen tasks; and its meta-control strategy for global coordination of all its behavior. We have designed and implemented an agent architecture that supports all of these different kinds of adaptation by exploiting a single underlying theoretical concept: An agent dynamically constructs explicit control plans to guide its choices among situation-triggered behaviors. The architecture has been used to build experimental agents for several AIS niches. We illustrate the architecture and its support for adaptation with examples from Guardian, an experimental agent for ICU monitoring.

A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We present a domain-specific software architecture (DSSA) that we have developed for a large application domain of adaptive intelligent systems (AISs). The DSSA provides: (a) an AIS reference architecture designed to meet the functional requirements shared by applications in this domain, (b) principles for decomposing expertise into highly reusable components, and (c) an application configuration method for selecting relevant components from a library and automatically configuring instances of those components in an instance of the architecture. The AIS reference architecture incorporates features of layered, pipe and filter, and blackboard style architectures. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture.>

chematically in Figure 1. It comprises a set of data structures (labeled boxes) in a global memory and a three-step execution cycle: (a) an executor executes the next action, making changes to memory and producing associated events; (b) an agenda manager triggers known actions whose conditions are satisfied by those events and puts contextspecific instances on an agenda of possible actions; and (c) a scheduler rates possible actions against the current control plan and chooses the one with the highest rating as the next action to be executed. Control plans play a central role in the architecture. As shown in Figure 1, the control plan is a data structure containing any number of component plans, each with its own hierarchical and temporal organization. Typical control plans are abstract and do not specify sequences of particular actions. Instead, each "step" in a plan specifies: (a) a class of intended actions in terms o

Given its multiple goals, limited resources, and dynamic environment, an intelligent agent must decide which of many possible actions to execute at each point in time. Planning and reactive models embody two different modes of control. By contrast, a 2D space of control modes is characterized, each of which maximizes the quality of run-time behavior in the corresponding region of a 2D space of control situations. The situation space is defined by dimensions representing the predictability of the agent's task environment and the contrast imposed by its goals. The space of control modes is defined by corresponding dimensions representing the agent's sensitivity to run-time events and its commitment to specific actions prepared in advance. The corners of the mode space are anchored by extreme modes that an agent rarely uses: open-loop plan execution, goal-specific reactivity, dead reckoning, and reflex modes. The large interior is occupied by a space of more practical strategic control modes that modulate the balance of sensitivity and commitment for situations embodying different combinations of predictability and constraint.>

The authors are developing a generic control architecture suitable for use as a single intelligent agent or as multiple cooperating agents. The generic architecture combines a task-oriented domain controller with a meta-controller that schedules activities within the domain controller. The domain controller provides functions for model-based situation assessment and planning, and inter-controller communication. Typically, these functions are performed by modules taken from a repository of reusable software. To improve the controller development process, the authors are combining many of the best ideas from software engineering and knowledge engineering in a software environment. This environment includes a blackboard-like development workspace to represent both the software under development and the software development process itself.>

Effective monitoring of device-supported patients in the intensive care unit (ICU) is complex, involving interpretation of many variables, comparative evaluation of many therapy options, and control of many patient-management parameters. Even skilled clinicians make errors that limit the quality of care, harm patients, or cause life-threatening situations. A growing body of research aims to improve ICU monitoring with computer technology. Most of this research falls in two areas: (a) short-term engineering of practical solutions to narrowly defined immediate problems (e.g. smart alarm systems); or (b) basic research on fundamental issues potentially relevant to ICU monitoring (e.g. temporal reasoning). By contrast, our project aims to develop a more comprehensive ‘intelligent agent’, having a broad range of capabilities, to cooperate on the ICU team. We do not aim to produce a practical system suitable for near-term deployment in the ICU, but rather a ‘proof of concept’, an experimental system that: (a) demonstrably performs and coordinates a range of intelligent reasoning tasks of use in ICU monitoring; (b) does so reliably in a significant range of medical situations; and (c) arguably will scale up to meet the comprehensive set of practical requirements with an appropriate development effort. We have developed an experimental system called Guardian, which exhibits several of the required capabilities and utilizes an underlying architecture hypothesized to support the full range of required capabilities. In this paper, we describe the Guardian system, its architecture, and its current knowledge base. We describe its performance and summarize the results of preliminary evaluations. Finally, we discuss ongoing and planned research on Guardian.