© 2004 Soar Technology, Inc.  July 15, 2015  Slide 1 Thinking… …inside the box Randolph M. Jones Knowledge-Intensive Agents in Defense Modeling and Simulation

© 2004 Soar Technology, Inc.  July 15, 2015  Slide 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

© 2004 Soar Technology, Inc.  July 15, 2015  Slide 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

© 2004 Soar Technology, Inc.  July 15, 2015  Slide 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

© 2004 Soar Technology, Inc.  July 15, 2015  Slide 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

© 2004 Soar Technology, Inc.  July 15, 2015  Slide 6 K-I Agents in Other Projects  User interface agents Cooperative Interface Agents for Networked Command, Control, and Communications (CIANC 3 )  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

© 2004 Soar Technology, Inc.  July 15, 2015  Slide 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

© 2004 Soar Technology, Inc.  July 15, 2015  Slide 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)

© 2004 Soar Technology, Inc.  July 15, 2015  Slide 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)

© 2004 Soar Technology, Inc.  July 15, 2015  Slide 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”