Operational Capability:We are developing and testing search munition control strategies using both a high fidelity 6-dof simulation of the LOCAAS and medium fidelity 6-dof simulation of an unspecified search munition. We are adapting team oriented programming approaches to provide sophisticated planning and cooperation capabilities to teams of munitions.

Adjustable Autonomy for the Battlefield Cooperative Attack Realtime Assessment (CAMRA)

Technical Approach:

We are developing techniques to allow wide area search munitions to cooperate in order to locate and attack targets, perform battle damage assessment, etc.Control concepts and prototype interfaces to allow humans to control and monitor cooperating search munitions

Agent Architecture

Four parallel threads:• Communicator

• for conversing with other agents

• Planner• matches “sensory”

input and “beliefs” to possible plan actions

• Scheduler• schedules “enabled”

plans for execution• Execution Monitor

• executes scheduled plan• swaps-out plans for

those with higher priorities

MAS Infrastructure Individual Agent Infrastructure

MAS InteroperationTranslation Services Interoperator Services

Capability to Agent MappingMiddle Agents

Name to Location MappingAgent Name Service

SecurityCertificate Authority Cryptographic Service

Performance ServicesMAS Monitoring Reputation Services

Multi-Agent Management ServicesLogging Activity Visualization Launching

ACL InfrastructurePublic Ontology Protocol Servers

Communications InfrastructureDiscovery Message Transfer

InteroperationInteroperation Modules

Capability to Agent MappingMiddle Agent Components

Name to Location MappingANS Component

SecuritySecurity Module Private/Public Keys

Performance ServicesPerformance Service Modules

Management ServicesLogging and Visualization Components

ACL InfrastructureParser, Private Ontology, Protocol Engine

Communication ModulesDiscovery Message Transfer Modules

Operating EnvironmentMachines, OS, Network, Multicast Transport Layer, TCP/IP, Wireless, Infrared, SSL

MAS Infrastructure

Functional Architecture

RETSINA A2A technology uses the existing Gnutella P2P network to gather information about agents, services and infrastructure components so that agents may connect across WANs to access each other’s services.

1. Hardwired Agent Communications

“I know you, the service you provide, and where you are located.”

In RETSINA, agents known to each other do not need centralized intermediaries to communicate.

2. ANS Location Registry

“I know who I want to speak with, I just need to find them. The agent I am looking for is in my local domain.”

Agent Name Server ANS)

The ANS is a server that acts as a registry or “white pages” of agents, storing agent names, host machines, and port numbers in its cache. The ANS helps to manage inter-agent communication by providing a mechanism for locating agents.

3. ANS Hierarchy Partners

“The agent I am looking for may not be in my local domain, so I will check with the ANS hierarchy partners with whom I am familiar. My partners will forward my request to their known partners, who will search their directories for me.”

Lookup Image-RecAgent

Image-Rec found!

Dynamic Discovery of infrastructure and agent services

New Middle-Agentsare discovered

across WAN, and the query propagates

within the LANs.

CMUAgentsGroup

Govt. Agencie s

agents

Software Engineering

Robotics Inst.

Computer Services

English Dept.

Image-Rec not found.

NASA

Lookup Image-RecAgent

Image-Rec found!

Dynamic Discovery of infrastructure and agent services

New Middle-Agentsare discovered

across WAN, and the query propagates

within the LANs.

CMUAgentsGroup

Govt. Agencie s

agents

Software Engineering

Robotics Inst.

Computer Services

English Dept.

Image-Rec not found.

NASA

5. Agent-to-Agent (A2A)

“I want to find agents, services and infrastructure beyond my LAN. I don’t know who or where these entities are, but I need to look for them across a Wide Area Network (WAN).”

4. Multicast Discovery--works best in Local Area Networks (LANs)

“Hello, my name is Agent B and my location is Y.”

“Hello, My name is Joe and my location is areolis.cimds.ri.cmu.edu and I can perform Y task.”

Multicast Discovery

ANS

ANS Matchmaker

ANS

“Hello, My name is Mike and my location is orion.andrew.cmu.edu and I can perform X task.”

Matchmaker

“Hello, My name is Joe and my location is areolis.cimds.ri.cmu.edu and I can perform Y task.”

Multicast Discovery

ANS

ANS Matchmaker

ANS

“Hello, My name is Mike and my location is orion.andrew.cmu.edu and I can perform X task.”

Matchmaker

With Multicast Discovery, agent registrations, locations and capabilities are “pushed” to other agents and infrastructure components, which discover each other and avail themselves to each other’s services.

Technical Approach:Develop new methods for high level information fusionLevel 2: force recognition (recognizing groups)Level 3: inferring intent and threatLevel 4: identifying & acquiring needed information•Developing simulation, display, and infrastructure for human-system interaction research•Conducting verification and validation studies with human users

Operational Capability:We have developed a suite of interacting tools using the OTB military simulation and the Unreal engine that allow us to simulate the warfighter’s environment anywhere on the battlefield. By combining ISR data, human communications, and realistic tasks we can test and evaluate conops and technologies for network centric warfare. Without the complexity allowed by these networked tools it would be impossible to test our research hypotheses involving active annunciation and information filtering and distribution. Simulation tools developed in this project have already been transitioned to AFRL and ARL laboratories and are in use at universities here and in Europe.

Information Fusion for Command and Control: from Data to Actionable Knowledge and Decision

AFOSR PRET F49640-01-1-0542

Convergent Tools CaveUT

Munitionsimulation

Robotic controlinterface

OTBsimulation

Terrainanalysis Unreal

engine

Subject Matter Expert’s MCOO (Modified Combined Obstacle Overlay)

Automatically generated by CMU’s terrain analysis software

Project Goal:Project Goal:To develop hybrid teams of autonomous heterogeneous agents—including cyber agents, robots, and humans—that intelligently coordinate and plan to accomplish urban search and rescue in disaster situations. We envision a Multi-Multi-Agent SystemAgent System (MASMAS) in which humans, agents, and robots work together seamlessly to provide aid as quickly and safely as possible in the event of an urban disaster.

USARUSARUrban Search and RescueUrban Search and Rescue

The RETSINA MAS Provides:The RETSINA MAS Provides:• Dynamic team coordinationDynamic team coordination, supporting teamwork between entities of varying capabilities;• Adjustable AutonomyAdjustable Autonomy for adaptively sharing control, responsibilities, and commitments at all task abstraction levels and by all types of team members (agents, robots, people);• Abstraction-based tiered robot architectureAbstraction-based tiered robot architecture that consists of incremental functional abstractions with real-time behavior based controllers at the lowest level, executive near-term explicit reasoning and scheduling at the middle level, and declarative planning and communication at the top level;• ScalabilityScalability to larger or smaller numbers of robotic and software agents without affecting the team goal through loss of coordination, etc.

http://www-2.cs.cmu.edu/~softagents/project_grants_NSF.html

VideoState Info

Corky

Telem

etry

Comm

ands

State In

foVideo

Telemetry Commands

Environment: disaster area

PERInterface

Human

Search and Rescue Results:Search and Rescue Results:

The Intelligent Software Agents LabKatia Sycara, Principal Investigator CoABS:

Effective Coordination of Multiple Intelligent Agents for Command and Control

Agents for collaboration in coalition environments

Agents construct and evaluate plans based on multi-dimensional effects and interactions among effects.

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