Blythe, Jim


Validating an Agent-Based Model of Human Password Behavior

AAAI Conferences

Effective reasoning about the impact of security policy decisions requires understanding how human users actually behave, rather than assuming desirable but incorrect behavior. Simulation could help with this reasoning, but it requires building computational models of the relevant human behavior and validating that these models match what humans actually do. In this paper we describe our progress on building agent-based models of human behavior with passwords, and we demonstrate how these models reproduce phenomena shown in the empirical literature.


Testing Cyber Security with Simulated Humans

AAAI Conferences

Human error is one of the most common causes of vulnerability in asecure system. However it is often overlooked when these systems aretested, partly because human tests are costly and very hard torepeat. We have developed a community of agents that test securesystems by running standard windows software while performingcollaborative group tasks, mimicking more realistic patterns ofcommunication and traffic, as well as human fatigue and errors. Thissystem is being deployed on a large cyber testing range. One keyattribute of humans is flexibility of response in order to achievetheir goals when unexpected events occur. Our agents use reactiveplanning within a BDI architecture to flexibly re-plan if needed.Since the agents are goal-oriented, we are able to measure the impactof cyber attacks on mission accomplishment, a more salient measure ofprotection than raw penetration. We show experimentally how the agentteams can be resilient under attacks that are partly successful, andalso how an organizational structure can lead to emergent propertiesof the traffic in the network.


EmoCog: Computational Integration of Emotion and Cognitive Architecture

AAAI Conferences

Since the reinvigoration of emotions research, many computationalmodels of emotion have been developed. None ofthese models, however, fully address the integration of emotiongeneration and emotional effect in the context of cognitiveprocesses. This paper seeks to unify various modelsof computational emotions while fully integrating with workdone in cognitive architectures. We propose a perspective onhow this integration would occur and EmoCog, a cognitivearchitecture with mechanisms for emotion generation and effects.


An Intelligent Personal Assistant for Task and Time Management

AI Magazine

We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences.


An Intelligent Personal Assistant for Task and Time Management

AI Magazine

We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences.


The Workshops at the Twentieth National Conference on Artificial Intelligence

AI Magazine

The AAAI-05 workshops were held on Saturday and Sunday, July 9-10, in Pittsburgh, Pennsylvania. The thirteen workshops were Contexts and Ontologies: Theory, Practice and Applications, Educational Data Mining, Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing, Human Comprehensible Machine Learning, Inference for Textual Question Answering, Integrating Planning into Scheduling, Learning in Computer Vision, Link Analysis, Mobile Robot Workshop, Modular Construction of Humanlike Intelligence, Multiagent Learning, Question Answering in Restricted Domains, and Spoken Language Understanding.


The Workshops at the Twentieth National Conference on Artificial Intelligence

AI Magazine

The AAAI-05 workshops were held on Saturday and Sunday, July 9-10, in Pittsburgh, Pennsylvania. The thirteen workshops were Contexts and Ontologies: Theory, Practice and Applications, Educational Data Mining, Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing, Human Comprehensible Machine Learning, Inference for Textual Question Answering, Integrating Planning into Scheduling, Learning in Computer Vision, Link Analysis, Mobile Robot Workshop, Modular Construction of Humanlike Intelligence, Multiagent Learning, Question Answering in Restricted Domains, and Spoken Language Understanding.


Decision-Theoretic Planning

AI Magazine

The recent advances in computer speed and algorithms for probabilistic inference have led to a resurgence of work on planning under uncertainty. The aim is to design AI planners for environments where there might be incomplete or faulty information, where actions might not always have the same results, and where there might be trade-offs between the different possible outcomes of a plan. Addressing uncertainty in AI, planning algorithms will greatly increase the range of potential applications, but there is plenty of work to be done before we see practical decision-theoretic planning systems. This article outlines some of the challenges that need to be overcome and surveys some of the recent work in the area.


Decision-Theoretic Planning

AI Magazine

The recent advances in computer speed and algorithms for probabilistic inference have led to a resurgence of work on planning under uncertainty. The aim is to design AI planners for environments where there might be incomplete or faulty information, where actions might not always have the same results, and where there might be trade-offs between the different possible outcomes of a plan. Addressing uncertainty in AI, planning algorithms will greatly increase the range of potential applications, but there is plenty of work to be done before we see practical decision-theoretic planning systems. This article outlines some of the challenges that need to be overcome and surveys some of the recent work in the area.