I-GLOBE: Distributed Planning and Coordination
I-Globe:
Distributed
Planning
and Coordination
of Team-oriented
Activities
in a Dynamic
Environment of
Mixed-initiative
Activities (thanks to ARO N62558-06-P-0353)
Q3 review report
Antonín Komenda, Jiří Vokřínek, Michal Pěchouček
Agent Technology Center, Gerstner Laboratory,
Czech Technical University in Prague
Gerhard Wickler, Jeff Dalton, Austin Tate
Artificial Intelligence Applications Institute
School of Informatics, The University of Edinburgh
KSCO 2009, 31st March and 1st April 2009, Southampton, UK
Content of the briefing
 Integration of ACROSS multi-agent simulation scenario (previously funded
by CERDEC, developed at ATG) and PACIFICA (previously DARPA funded
scenario, developed at AIAI)
IGLOBE Scenario
 disaster relief scenario, based on Pacifica, extended with
» low-level planning & simulation, rich behavioural models of the actors
» detailed model of the environment, destructible entities and others
» Dynamic interaction among the actors (=resources)
 discrete simulation, 100x accelerated, region scale ~ 300km, 1 time tick ~ 100ms
ACTORS
GOALS
build houses,
commanders,
heal injuries,
ground transport units,
build mobile hospital,
aerial transport units,
explore area,
surveillance UAV,
provide transportation,
surveillance HTUAV,
unit tracking.
surveillance helicopter,
builders, medical doctors,
construction resource providers,
medical material providers.
PRIMITIVE ACTIONS
build structure,
heal injured,
take snapshot,
move to/fly to,
load resources/unit,
unload resources/unit,
transport unit,
transport resources,
handover goal,
follow unit.
Research Issues and Assumptions
 Assumptions:
» actors may have conflicting objectives, they have their private agenda
» there is little predifined planning hierarchy, it emerges from interaction
» resources overbooking can occur and execution may cause replanning
» planning process is processed on three levels:
» strategic: setting and planning set of mission objectives
» tactical: delegation and task assignment
» individual: planning individual activity (such as trajectory)
 The key research concepts we have been focused on are:
» distributed planning : responsibility allocation, collaborative plan
formation, plan merging and plan synchronization
» distributed resource allocation: negotiation among t the actors about
available resources and their utilization in time
» distributed plan execution: distributed execution of plans, plan
accomplishment monitoring and negotiation oriented replanning
Approach
1.
System integration:
» I-X, AGLOBE, ACROSS and AgentFly (in parts funded by DoD)
2. Theoretical multi-agent concepts:
» Extension of the concept of social commitments, as the
specific knowledge representation for integrating the
planning and multi-agent resource allocation
3. Experimental validation on a multi-agent simulation:
» Studying how the social commitments can contribute to
increased robustness of the whole distributed plan.
HTN Planning in I-X
 I-X provides an issue-handling style of architecture, with reasoning and
functional capabilities provided as plug-ins. Also via plug-ins it allows for
sophisticated constraint management, and a wide range of
communications and visualisation capabilities.
 A More Intelligible Framework for Collaborative Planning:
» Human relatable and presentable objective statements, advice and
plan representations for outer levels
» Detailed search engines, constraint solvers,
Process Panel
Activity Editor
analyzers and simulations act in this
framework in an understandable
way to provide feasibility checks,
detailed constraints and guidance
» Sharing of processes and
process products via <I-N-C-A>
Domain Editor
Messenger
I-Space
I-X Architecture Integration
The I-X architecture is used in I-Globe on several levels:
 the I-X I-Plan planner is used as the main HTN planner of the system
 each entity uses its own instance
 Multi-layer Planning Architecture uses the I-Plan on the Strategic Layer
 the Commander actors uses I-P2 (I-X Control Panel)
 allows mixed-initiative planning
 allows monitoring based on the
state subscription interfaces and
protocols
 the Sense Maker actor uses I-P2
 for tasking of the Commander
actors
 as a superior interface for the
I-X Control Panels of the
Commanders
AGENTFLY Accelerated A* space/time planning
Planning problem in AGENTFLY is to find an optimal flight plan (sequence of
elements defining the smooth path for the UAV) respecting all physical airplane
constraints and fulfils specified mission (ordered sequence of way-points
including optional time and cruise speed limits). The given flight plan can utilize
only available airspace for the airplane (respects defined no-flight zones).
Implemented as two phases planning with backtracking:
» spatial planning – prepares the spatial part (3D plan) of the flight plan
• A* with state space is given by chaining of usable flight plan elements
• accelerated by using hierarchical dynamic size of the searching step
» time planning – adds the fourth dimension to the flight plan:
• 3D plan and optional-waypoint constraints are transformed to 1D
problem (distance in time)
• identify unsolvable parts and re-invoke appropriate backtracking to
spatial planning (e.g. cannot slow down more -> insert holding orbits)
Accelerated A* space/time planning
Optimized airspace definition based
on combination of transformed
components:
» octant tree structure
» height map
» composition of boxes,
ellipsoids and cylinders
Fast operation for:
» test ellipsoid intersection
» test corridor box intersection
AGENTFLY Demonstration
AGLOBE simulation/integration framework
 Advanced, scalable and robust multi-agent simulation environment,
developed under partial support of AFRL/EOARD and CERDEDC/ARO.
 compared to other multi-agent platforms, AGLOBE offers
» superior performance, low overhead, distributed load balancing
» full agent advanced agent mobility and computational reflection
» environmental modeling and scenario simulation support
» complex visualization environment
 AGLOBE is suitable for development of large scale simulation scenarios
and can be seamlessly ported to real-world deployment platforms using a
standard methodology. key functionalities:
» Modeling and simulation
» Demo/visualization support
» Scalable experimental testing
» Hardware deployment support
 Deployment in air-traffic control, distributed diagnostics, adversarial
planning, design process modeling, collective underwater robotics, etc
Social Commitments
 The integration between HTN, MAP, and multi-agent simulation (or
operation monitoring) is implemented by means of social commitments –
derived from social commitments suggested by [Jennings].
 Social commitment is a special knowledge structure that represents a
conditional intent of an actor to implement an action (primitive or
complex) and specifies nontrivial rules of decommitment.
» Individual commitments is often used for intentional modeling and
programming of intelligent agents.
» Joint commitments represent an agreement between two or more
actors with respect to either a shared goal or mutually dependent
individual goals.
» integration between human, robots and different planning tools
» dynamic adjustment and control of reliability of interaction and
» flexible, multi-level re-planning and dynamic coordination
Programming commitments in I-GLOBE
 Use of commitments in I-GLOBE is motivated by establishing a transparent
and efficient coordination mechanism for distributed planning in I-GLOBE
 Use of formal analysis of the individual commitments remains an option
 Social commitment as a knowledge containing:
» task specification, preconditions and postconditions,
» references to other commitments (oriented commitment graph),
» decommitment rules organized in 3 explicit levels:
• Full decommitment – mere report of a failure to achieve the
commitment: termination condition, blind decommitment, JPG –
Joint persistence goal
• Commitment Relaxation – individual change of the quality of
service, to be proposed to the service requestor (preagreed)
• Commitment Delegation – search for another actor who is
available to implement the failed commitment
» explicit reference to the commitment participators
Programming commitments in I-GLOBE
 Commitments and planning? In two phases:
1. Negotiation about commitment proposals
• multi-criteria optimization: quality of service (due time, costs) vs.
decommitment flexibility
• e.g. low flexibility commitments disallow commitment relaxation
while high flexibility commitments try to avoid full decommitment
2. Implementation of the commitment selection and confirmation
• deployment of the decommitment rules (either fixed or subject of
further negotiation (such as commitment delegation)
Evaluation - -Different
decommitment
rules
Evaluation
Execution
Time
Evaluation - -Different
decommitment
rules
Evaluation
Execution
Time
Evaluation - -Execution
time
Evaluation
Execution
Time
Evaluation - -Negotiation
Complexity
Evaluation
Negotiation
complexity
IGLOBE Demonstration
IGLOBE Demonstration
Future work
Summary
 commitment-based coordination and AA* planning is further developed in:
» TACTICTAL AGENTFLY (W911NF-08-1-0521_1312AM01): Intelligent
Software Agent Control of Combined UAV Operations for Tactical Missions
(CERDEC and EOARD co-funded)
» MANET AGENTFLY (W911NF-08-1-0505_1317CE01): Agent-based Control
for Connectivity Maintenance of Tactical MANETs (CERDEC funded)
 Based on IGLOBE ACROSS scenario the DEEPA testbed has been implemented:
» funded by EOARD and AFRL (FA8655-07-1-3083)
» used for testing adversarial planning
 integration of distributed constrain satisfaction techniques in collaboration
with Drexel University and ACIN
 new critical challenge: how to integrate an automated scene reconstruction
from 3D with the distributed planning and replanning capabilities – the whitepaper for ONR in preparation (in collaboration with CMP)
 interaction in semi-trusted environment – adjustable privacy
Tactical AGENTFLY