DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

Understanding Agent Societies Using Distributed Monitoring and Profiling
† Wilbur Peng, † Vikram Manikonda, and ‡ Soundar Kumara
† Intelligent Automation Incorporated
7519 Standish Place, Suite 200, Rockville, MD 20855
{wpeng ,vikram}@i-a-i.com
‡
Industrial and Manufacturing Engineering
310 Leonhard Building, The Pennsylvania State University, University Park, PA 16802
{skumara}@psu.edu
Abstract
In this paper, we describe methodologies for
understanding large-scale agent societies using the
Castellan, a distributed profiling and logging system
developed for Cougaar. Castellan enables the detailed
efficient logging of blackboard plan activity. We describe
the design, functionality, use and a number of
applications of the Castellan tool, including a
visualization and data mining tool based on a flexible
algorithm for finding subgraph isomorphisms. By
mapping “equivalent” meaningful graph nodes and edges
to representative subgraph elements, the graph reduction
approach reduces large plan graphs of hundreds of
thousands to millions of nodes to meaningful and
understandable clusters and graph nodes. This algorithm
is demonstrated through its application to event traces
obtained from running Castellan within a military
logistics planning society. In addition to providing data
for static analysis after planning and execution, the
Castellan approach is also useful for on-line analysis of
active, running agent systems. We also describe a
number of other potential applications of distributed
monitoring for modeling, control, load balancing and
analysis.
1. Introduction
Distributed agent systems provide significant challenges
for debugging, testing, profiling and tuning. Agent
societies consist of distributed, state encapsulated entities
that can run concurrently. Additionally, they have the
additional constraint of state encapsulation, i.e. each
agent does not have direct access to the state of other
agents. Instead, they interact solely through message
passing. Within an agent, different functionscan interact
through sharing state.
The Cougaar agent infrastructure supports an approach to
distributed planning in which tasks are created and
expanded into subtasks by agents which can in turn be
forwarded to other agents. The planning process creates a
plan graph that spans multiple agents that can potentially
be very large, growing to hundreds thousands to millions
of elements. Adding to the complexity of understanding
system function, the plan graph generated by the agents
can be dynamically modified during the planning and
execution phases of the society. As Cougaar agent
societies increase in size and scope, understanding the
distributed execution of the system becomes increasingly
difficult. Being able to trace the time-evolving, eventdriven
behavior across agents running societies becomes
increasingly important.
In this paper, we discuss methods for understanding,
analyzing and controlling Cougaar agent societies
through distributed profiling. Section 1.1 covers
background concepts in distributed planning used by
Cougaar. In Section 2, the Castellan profiling and logging
system is introduced and its implementation and design
described. Section 3 presents in detail an application of
Castellan to data mining and visualization application
using a plan graph reduction algorithm. Finally, Section
4 discusses potential applications of Castellan.
1.1 Distributed plan graphs in Cougaar societies
In this section, we review some basic concepts of
planning in the Cougaar context. Additional details about
plan representation can be found in [3].
In Cougaar applications such as logistics planning and
execution, agents generate plans by decomposing tasks
into subtasks, aggregating tasks, and forwarding tasks to
other organization entities which are in turn are
represented by other agents.