SRTA: The SoftReal Time Agent Control Architecture

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SRTAThe Soft-Real TimeAgent Control
Architecture

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

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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

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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

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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

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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

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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
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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

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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

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Soft Real-Time Architecture
Problem solver
Other Agents
Negotiation
Reasoning
Goal Description
Update Expectations
TÆMS Library
TÆMS Structure
DTC-Planner
Learning
Resource Modeler
Resource Uses
SRTA
Linear Plan
Schedule Failure
Partial Order Scheduler
Conflict Resolution Module
Schedule Failure
Multiple Structures
Parallel Schedule
Task Merging
Parallel Execution Module
Results
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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) )
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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
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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

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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 DTCs existing expertise

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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

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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

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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

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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