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April 28, 2004 April 28, 2004 John C. Giordano – Masters Project John C. Giordano – Masters Project 1 Exploring the Constraints of Human Behavior Representation A Masters Project Presentation John C. Giordano Prof. Paul Reynolds - Advisor
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April 28, 2004John C. Giordano – Masters Project Presentation1 Exploring the Constraints of Human Behavior Representation A Masters Project Presentation.

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Page 1: April 28, 2004John C. Giordano – Masters Project Presentation1 Exploring the Constraints of Human Behavior Representation A Masters Project Presentation.

April 28, 2004April 28, 2004 John C. Giordano – Masters Project PresentationJohn C. Giordano – Masters Project Presentation 11

Exploring the Constraints of Human Behavior Representation

A Masters Project Presentation

John C. Giordano

Prof. Paul Reynolds - Advisor

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Outline

• Introduction & problem statement

• Key terms

• Highlights of literature review

• Findings

• Proposed framework for considering human behavior representation (HBR) capabilities

• Conclusions

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“We can only see a short distance ahead, but we can see plenty there that needs to be done.”

Alan Turing

Computing Machinery and Intelligence

In Mind: 236, 1950

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Introduction

• In 1950, Alan Turing proposes the Imitation Game

• Machines competing with or replacing humans

• Human behavior representation (HBR) refers to the portrayal of humans

• HBR is not Artificial Intelligence– More constrained– Still a challenge

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What is the Problem?

• HBR is critical to many, but has proven elusive

• Several large-scale development failures with prominent HBR requirements– DoD’s Joint Simulation System (JSIMS)– NASA’s Air Traffic Management (ATM) simulation

• Shortcomings noted by many in the community

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How We Attempt to Address It

• Examined successes and failures in research, design and implementation

• Describe what is currently attainable and propose what is unachievable

• Present a framework for assessing HBR capabilities

• Seeking publication of research conducted to date

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Key Terms

• HBR: a computer-based model that mimics either the behavior of a single human or the collective action of a team of humans

• Intelligent Software Agent: an artificial agent that operates in a software environment and imitates human intelligence by mechanical means in pursuit of the goals of its clients

• Human Cognition: the process of receiving, processing, storing, and using information in humans

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Literature Review

• Over 60 publications (papers, journal articles, texts, tech reports, requirements documents)

• Extended annotated bibliography

• Thorough, but not fully exhaustive

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Literature Review

• Modeling Human and Organizational Behavior. Richard W. Pew and Anne S. Mavor (eds.). National Academy Press, Washington DC, 1998.

• Techniques for Modeling Human Performance in Synthetic Environments: A Supplemental Review. Frank E. Ritter, et al. Human Systems Information Analysis Center, Wright Patterson AFB, OH, 2002.

• A Taxonomy of Human Behavior Representation Requirements. Scott Y. Harmon. 11th Conference on Computer Generated Forces and Behavior Representation, 2002.

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Harmon’s Taxonomy

Human Representation

Non-Cognitive Factors

Cognitive Capabilities

Application Functions

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Harmon’s Taxonomy

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Harmon’s Taxonomy

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Findings

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Findings

• The tools used to model and simulate HBR are constrained

• The phenomena associated with HBR are highly complex

• At times, HBR requirements vastly exceed capabilities

• Capabilities and constraints should be clearly articulated to the community

• Some capabilities may (only) be attained with the emergence of a disruptive technology

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Some Tools

– Soar: general cognitive architecture for intelligent agents

– COGNET/iGEN: emulator for human decision-making and problem-solving

– ACT-R: architecture for human cognition

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Three Categories of HBR Capabilities

Mature Developing Unachievable in practice

Constrained speech recognition, parsing and generation X

Course of Action (COA) analysis, selection and implementation X

Rudimentary emotions X

Human physiological characteristics X

Semi-automated coarse-grained behavior generation X

Probabilistic human performance simulation and prediction X

Autonomous, convincing group behavior X

COA generation X

Interdependence between physiology, emotion and cognition X

Behavior adaptation appropriate to dynamic scenarios X X

Speech generation w/ appropriate prosody X X

Pattern recognition coupled w/ appropriate decision-making X X

Generalized behavior prediction X X

A single framework for modeling human behavior at multiple levels of resolution X X

Complex cognition, reasoning and learning X

Conversational dialogue X

Synthesis of autonomous knowledge acquisition, planning and behavior X

Complete integration between emotion, cognition and behavior X

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Constrained speech recognition, parsing and generation

Course of Action (COA) analysis, selection and implementation

Rudimentary emotions

Human physiological characteristics

Semi-automated coarse-grained behavior generation

Probabilistic human performance simulation and prediction

Mature Capabilities

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Autonomous, convincing group behavior

COA generation

Interdependence between physiology, emotion and cognition

Developing Capabilities

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Behavior adaptation appropriate to dynamic scenarios

Speech generation w/ appropriate prosody

Pattern recognition coupled w/ appropriate decision-making

Generalized behavior prediction

A single framework for modeling human behavior at multiple levels of resolution

Developing and Unachievable

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Complex cognition, reasoning and learning Conversational dialogue

Synthesis of autonomous knowledge acquisition, planning and behavior

Complete integration between emotion, cognition and behavior

Unachievable in Practice

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A Generational Framework forConsidering HBR Capabilities

• Analogous to the generations of programming languages

• Generations– 1st: speech recognition, rudimentary emotions/physiology,

probabilistic performance

– 2nd: domain-independent speech, COA generation, adaptive behaviors

– 3rd: single cognitive framework, architecture for multi-resolution behavior modeling, etc.

– 4th: approaching human faculties

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A Generational Framework forConsidering HBR Capabilities

General

Modeled Phenomenon

Measurable Not Measurable

Model Specificity

Speech recognition Integrated emotion and cognition

Conversational dialogue

Autonomous learning and planning

Concrete

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Conclusions

• Turing foresaw human-machine competition – HBR comprises portrayal

• Requirements development needs improvement

• Opportunities for continued research

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Future Work

• Update and extend Pew and Mavor, Ritter et al.

• Focus on HBR successes, particularly with promise of generalization

• Rigorous, formalized HBR requirements

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Questions?

“It takes some philosophical discipline, in short, to resist specious blurrings of differences between simulations and the phenomena they simulate.”

Larry Crockett

In The Turing Test and the Frame Problem: AI’s Mistaken Understanding of Intelligence

Intellect Books, 1994