Top-Down and Bottom-Up Scheduling in Swarm

Top-Down and Bottom-Up Scheduling in Swarm A comparison of implementations Paul E. Johnson University of Kansas

Overview • Agent-Based Modeling and the “bottom-up” objective • Top-down scheduling • Bottom-up scheduling strategies • Verification

ABM and bottom-up modeling • Autonomous individuals • Limited information • Experiential learning • Polymorphic modeling

But Scheduling is still Top-Down • Standard cellular automaton. • “Freeze” a snapshot of the grid • Update cells against snapshot • Flush all updated cells onto grid • Repeat

Examine Many Swarm Apps • Agent design/philosophy is bottom-up • Scheduling is top-down • Agents are kept in a list • They “step” (do something”) when told to do so. • Agents have no notion of “time” although they are aware of things changing

Heatbugs • Traditional Swarm-1.0 approach • createActionForEach actionForEach = [modelActions createActionForEach: heatbugList message: M(step)]; • Swarm-1.0.5 introduced “randomized” traversal of the list by allowing [actionForEach setDefaultOrder: Randomized];

Heatbugs-2.1 • FAction framework • Create a “call” object and then have each agent respond to that call. id call = [Fcall create ….]; actionForEach = [modelActions createFActionForEachHomogeneous: heatbugList call: call];

Bottom-up Scheduling • Ideal world: agents “decide for themselves” when to act • Discrete Sim Libraries must integrate these many disparate behaviors so that they “fall into a common time line” • Time is a conveyor belt:

Harmonize actions across levels

2 Approaches for B-U Scheduling • Master Schedule • Decentralized Autonomous Schedules

Master B-U Scheduling • Create one Schedule object in ModelSwarm level • In each agent, at create time, tell agent about Schedule. - setSchedule: id <Schedule> aSched; • Agent’s step method: • Carry out actions for “current time” and • Place “step” method on schedule for future time.

Master Schedule in Mousetrap • Mousetrap “dynamic schedule” is an intermediate example • One Schedule created in Model Swarm • Agent’s (mouse traps) are not aware of Schedule, but instead they “trigger” and tell Model to trigger some other trap at some future time [modelSchedule at: n createActionTo: trap message: M(trigger)];

Complication: Concurrency • What if several agents “schedule themselves” at a given time? • Ordinarily, actions in Swarm schedules are ‘first come, first serve’ • Possible to randomize actions when a cell is reached: Concurrent Group Options

Concurrent Randomization id groupProto = [ConcurrentGroup customizeBegin: self]; [groupProto setDefaultOrder: Randomized]; groupProto = [groupProto customizeEnd]; agentSchedule = [Schedule createBegin: self]; [agentSchedule setConcurrentGroupType: groupProto]; [agentSchedule setAutoDrop: YES]; agentSchedule = [agentSchedule createEnd];

Decentralized B-U Scheduling • Each agent is a Swarm with a Schedule • Activate each within “agent swarm” • Agent tells self to “step” at future a future time point by putting action on its own schedule. • Swarm able to integrate actions across all agents.

activateIn: is magic When a schedule is activated in a Swarm, each time step (t): “clears” all the actions on that cell at t And It traverses all time t cells in all Swarms that are activated in it

How Does Swarm Do it? • Synchronization is the key word • At each time step, each Swarm “scans” all lower Swarms to see if they have actions to be executed. • Default: “first on, first off”

Randomization Customization • (post Swarm-2.1.1) • pjrepeater*.m • Step 1/5: create AgentSwarm (a container) • Step 2/5: customize ConcurrentGroup (same as previous groupProto)

Customize “container swarm” • Step 3/5: customize a Schedule object syncSched = [Schedule customizeBegin: self]; [syncSched setConcurrentGroupType: groupProto]; [syncSched setAutoDrop: 1]; syncSched = [syncScheduleProto customizeEnd];

Set Sync Type! • Step 4/5: customize the “container Swarm” with Schedule agentSwarm = [AgentSwarm createBegin: self]; [agentSwarm setSynchronizationType: syncSched]; agentSwarm = [agentSwarm createEnd];

Put agents into context • Step 5/5: Activate citizens inside context of container (AgentSwarm). [citizenList forEach: M(activateIn:):self];

How do we know it works? • Take a complicated model • Design it to compare both implementations • Compare results!

My Monster Opinion Project • Agents move, interact, adjust in here:

Time passes in days • Each day has 10 timesteps • Agents can go and come back • Randomly decide to interact. • “Catch-as-catch-can” interactions • Compare Master Schedule versus Fully Decentralized Scheduling • 500 runs for each type (same seeds)

Conclusions • Either implementation is fine! • Master Schedule simpler to implement • Decentralized Scheduling slower!

Top-Down and Bottom-Up Scheduling in Swarm