MAS Infrastructure. Agents for collaboration in coalition environments. Functional Architecture. Agent Architecture. Four parallel threads : Communicator for conversing with other agents Planner matches “sensory” input and “beliefs” to possible plan actions Scheduler
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Agents for collaboration in coalition environments
Translation Services Interoperator Services
Capability to Agent Mapping
Capability to Agent Mapping
Middle Agent Components
Name to Location Mapping
Agent Name Service
Name to Location Mapping
Adjustable Autonomy for the Battlefield
Cooperative Attack Realtime Assessment (CAMRA)
Certificate Authority Cryptographic Service
Security Module Private/Public Keys
MAS Monitoring Reputation Services
Performance Service Modules
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.
Multi-Agent Management Services
Logging Activity Visualization Launching
Logging and Visualization Components
Public Ontology Protocol Servers
Parser, Private Ontology, Protocol Engine
Discovery Message Transfer
Discovery Message Transfer Modules
Individual Agent Infrastructure
Machines, OS, Network, Multicast Transport Layer, TCP/IP, Wireless, Infrared, SSL
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
Search and Rescue Results:
1. Hardwired Agent Communications
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.
“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.
Environment: disaster area
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
4. Multicast Discovery--works best in Local Area Networks (LANs)
“Hello, my name is Agent B and my location is Y.”
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).”
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.
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.
The Intelligent Software Agents LabKatia Sycara, Principal Investigator
Ready-to-use software-integration-web technologies
Effective Coordination of Multiple Intelligent Agents for Command and Control
Information Fusion for Command and Control: from Data to Actionable Knowledge and Decision
AFOSR PRET F49640-01-1-0542
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.
Automatically generated by CMU’s terrain analysis software
Subject Matter Expert’s MCOO (Modified Combined Obstacle Overlay)
Agents construct and evaluate plans based on multi-dimensional effects and interactions among effects.
USARUrban Search and Rescue
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-Agent System (MAS) 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.