Knowledge-Intensive Agents in Defense Modeling and Simulation

1. Thinking… …inside the box Knowledge-Intensive Agents in Defense Modeling and Simulation Randolph M. Jones

2. What is a Knowledge-Intensive Agent? • A software system that • Is interactive with an external environment • Incorporates a fairly large body of long-term knowledge • Introduces unique concerns about the organization, implementation, and run-time use of knowledge • Creates an maintains significant internal representations of its situational understanding • This summary also covers agents with smaller knowledge bases, but that are designed in the same spirit as K-I agents

3. Features of K-I Agents • Knowledge representation must support relational pattern-based matching and retrieval • Long-term knowledge must be retrieved associatively and efficiently • Run-time data representations must support multi-valued relations (and pattern matching) • Some form of truth maintenance system should support efficiency and consistency in situational representations • For maintainability and extensibility (and perhaps efficiency), knowledge representation language must support alternative high-level organizations (e.g., goals, beliefs, problem spaces) • Logic flow of decision making must be re-entrant and use least commitment

4. K-I Agents in Soar Technology M&S Projects • JFCOM • Joint Urban Operations Human-In-The-Loop Experiment • SOF-Soar agents masquerade as civilians, recon enemy forces • Move along roads using JSAF path planning libraries • Pass contact information to SLAMEM sensor modeling system • Accept user tasking from JSAF GUI • Display planned and used routes on JSAF GUI • Enduring Freedom Reconstruction • SOF-Soar agents call in air strikes to TacAir-Soar agents • TacAir-Soar agents add behaviors for strafing, laser-guided CAS, bomb patterns, B-52 missions • SOF-Soar integration with DI-Guy to provide high resolution visual representation of individual combatants • Fleet Battle Experiment/Millennium Challenge ’02 • TacAir-Soar agents integrate with TBMCS to receive air tasking orders, launch from carriers, fly naval air missions

5. K-I Agents in Soar Technology M&S Projects • Automated Wingman • Provide Army helicopter teammates for human pilots in experimental scenarios • SOF Air Ground Interface Simulation (SAGIS) • Provide Close-Air Support and Indirect Fire behaviors to support training of Terminal Air Controllers • Advanced Global Intelligence and Leadership Environment (AGILE) • Simulate national or organizational decision making • General dynamics scout robot (and simulation) • Soar integration to GDRS control architecture • Team coordination, route planning/following

6. K-I Agents in Other Projects • User interface agents • Cooperative Interface Agents for Networked Command, Control, and Communications (CIANC3) • Assist command, control, and communication during mission execution • Battlespace Information and Negotiation through Adaptive Heuristics (BINAH) • Provide data fusion and information display for time critical targeting • Knowledge Enablers for the Unit of Action (KEUA) • Determine requirements, acquire, fuse, and present information to support command decision making • VISTA Explanation Agents • Architecture-neutral facility for communicating agent behavior explanations to end users through the VISualization Toolkit for Agents

7. “Knowledge/Agent Components” • Soar Technology Goal System (STGS) • Declarative means-ends-analysis style representations of goal and operator trees • Onto2Soar • Structured declarative knowledge with compiler to procedural Soar productions (CIANC) • Communications infrastructure • Layered transport- and content-neutral processing of agent communications (VIRTE, TacAir-Soar, Automated Wingman, SAGIS) • SoarSpeak • Speech recognition/generation integrated with Soar

8. “Knowledge/Agent Components” • High Level Symbolic Representation language • High level object- , agent-, and symbol-oriented behavior language with compiler to Soar productions • TCL-based parser providing HLSR prototype implementation • Qualitative spatio-temporal models/representation • Structured representation and reasoning over events in real time and projected time (Augmented Warrior, BINAH, HLSR) • Qualitative spatial representations and reasoning (BINAH) • End-user specification language for data fusion, display, and high-level knowledge design with compiler to Soar productions (AGILE, BINAH) • Deontic reasoning infrastructure • Structured representations of authority, obligation, permission, responsibility for teamwork (CIANC)

9. Nuggets • Soar directly supports much of the essential low-level functionality for K-I agents • We are using Soar to build a wide variety of K-I agents • We are developing a number of supporting components and technologies for developing K-I agents (within Soar and otherwise)

10. Lumps • We still need improved high-level organizations and tools for managing large agent knowledge-bases • Customer enthusiasm for K-I agents waxes and wanes • Most of the supporting components and technologies are not yet “ready for prime time”