SRTA:

The Soft-Real Time Agent Control Architecture

Bryan Horling, Victor Lesser, Regis Vincent, Thomas Wagner presented by Anita Raja

Agent Control

 Most multi-agent research addresses inter agent activities  The intra-agent mechanics are just as important…  Control affects the potential level of flexibility and sophistication for entire agent  Generality, efficiency, reliability…  Solid general control architecture provides foundation for further research

Motivation

   Several existing research artifacts…  Task modeling   Planning and scheduling Coordination  Previous work done in simulation New more demanding domain (ANTs)  Real-time   Uncertain Resource-bound  More realistic conditions Desire to merge these technologies into a cohesive, functional, reusable entity

Soft Real-Time Architecture

 Plan and schedule to solve goals  Resource constraints  Task interaction constraints  Deadlines and earliest start times  Merge new goals with existing ones  Adjust interleaved schedules as necessary  Handle unexpected deviations in execution  Address time-related failures  Resolve conflicts from failed actions

Soft Real-Time

 Hard real-time: formally bound and quantitatively describe performance  Soft real-time is a looser metric   Tasks may still have value if time bounds are exceeded by small amount Our interest is to be statistically “fast enough”  Can target more uncertain domains  Better handle unexpected events  For motivating domain, tasks should be performed within ±500ms of scheduled time

SRTA Context

  Operates at the middle agent layer API formed of two parts…   Function accessors TÆMS modeling language  Comprised of several components  Co-exists in JAF framework with other components Domain Problem Solver Soft Real Time Architecture JAF Controller

TÆMS Task Structures

     TÆMS is a goal decomposition planning language Tasks represent goals or sub-goals Methods are primitive actions that can be performed QAFs dictate how tasks accrue quality Interrelationships specify interactions between nodes

Java Agent Framework

 Component-based design, similar to JavaBeans  Individual components are well-encapsulated and potentially ‘autonomous’  Components organized much like a miniature multi-agent system  Intra-agent interactions in the form of  Direct method invocation  Indirect common data handling  Event delivery and receipt

Soft Real-Time Architecture

Other Agents Negotiation Problem solver

Goal Description

TÆMS Library

TÆMS Structure Update Expectations

Resource Modeler

Resource Uses

Conflict Resolution Module

Schedule Failure Multiple Structures

Task Merging DTC-Planner Learning

Linear Plan

Partial Order Scheduler

Parallel Schedule Schedule Failure

Parallel Execution Module

Results

Goal Instantiation

 Goals are represented using TÆMS  May be dynamically created, or read from static files  pTÆMS allows for parameterized, template-like structure definitions (spec_method (label Track-Medium) (agent Agent_A) (supertasks Track) (earliest_start_time 500) (deadline 2000) (outcomes (Outcome (density 1.0) (quality_distribution 5.0 0.5 1.0 0.5) (duration_distribution 750.0 1.0) (cost_distribution 0.0 1.0) ) ) ) (spec_commitment (label commitment-1) (type deadline) (from_agent Agent_A) (to_agent Agent_B) (task Track) (earliest_start_time 500) (deadline 2000) )

Planning

(developed by Tom Wagner)

   Goal planning by Design-to-Criteria scheduler Select the most appropriate set of end-to-end actions from a structure  Considers action and plan duration, quality, cost, interrelationships, constraints  Reasons about mandatory and optional requirements, with respect to desired plan criteria Differentiated by reasoning over ‘soft’ conditions Slider Criteria/ Importance Model Raw Goodness 100% Qualit y Cost Durat ion Thresholds/Limits Qualit y Cost 100% Durat ion Qualit y Certainty Cost Durat ion Certainty Thresholds Qualit y Cost Durat ion 100% Meta Raw Goodness T hresholds/ Limit s Cert aint y Cert aint y T hresholds 0% 66% 33% T hreshold Limit $5.75

Limit 50% 50% T hreshold T hreshold T hreshold 80% 25% 25% 25% 25%

Scheduling

 DTC was designed as a single-structure scheduler  Multiple goal structures must be merged, or assumed independent  Merged structures are larger, slower to schedule  Goal independence is an impractical condition  A more flexible approach is needed

Partially Ordered Scheduling

(developed by Regis Vincent)

 Partial ordered scheduler analyses DTC plans  Determines task-based precedence constraints  Resource modeler detects resource constraints   Builds a precedence graph, used for scheduling and rescheduling Key: Leverage DTC’s existing expertise Structure Init Calibrate Set-Parameters Negotiate-Tracking Send-Message Track-Medium Send-Tracking-Info Static Relationship Send-Result 2500 Dynamic Relationship

Resource Modeler

 Creates and maintains timeline of expected uses of resources  Distribution based: probabilistic start time, duration and quantity consumed or produced  Used by scheduler to find and bind appropriate times for methods  Used by execution component to monitor resource level expectations

Schedule Merging

 STRA natively supports multiple concurrent, interdependent goals  PO Scheduler considers prior precedence graphs when scheduling new tasks  Conflicts avoided by “shifting” methods based on graph information  Avoids monolithic rescheduling  …but retains the flexibility to modify prior scheduling results as needed

Execution

 Method execution is assumed to be in parallel  Constraints (resource, interrelationships, etc.) are validated before method is started  Failed constraints require rescheduling  PO Scheduler precedence graphs are again used for quick shifting where possible  Results are reported to other components and checked for failures

Future Work

 Provide an end-to-end model of performance bounds  Add anytime character to techniques  Meta-level reasoning system to control level of effort and resource expenditure