“Agent seeks food.”

This section provides a step-by-step walkthrough of a simplified scenario to illustrate how the previously defined components and systems interact to produce agent behavior. Our example agent, “Herbivore-01” (H-01), will seek food.

Initial Agent State (H-01 at Tick 0)

(As defined in Section 3.4.1)

• SustenanceNeed:
  • current_value_at_last_update: 0.7
  • last_updated_tick: 0
  • decay_rate_per_tick: 0.0001
  • goal_trigger_threshold: 0.4
  • critical_threshold: 0.1
  • _scheduled_goal_trigger_event_id: Event_A (Scheduled for Tick 3000, calculated: (0.7 - 0.4) / 0.0001 = 3000)
  • _scheduled_critical_event_id: Event_B (Scheduled for Tick 6000, calculated: (0.7 - 0.1) / 0.0001 = 6000)
• AgentBeliefStore (Relevant Beliefs):
  • Belief(subject_type=RedBerryBush_Type, property=IS_EDIBLE, value=true, strength=0.6, confidence=0.5, source_type=INNATE)
  • Belief(subject_type=RedBerryBush_Type, property=HAS_MODERATE_SATIATION_VALUE, value=true, strength=0.5, confidence=0.4, source_type=INNATE)
  • Belief(subject_type=WolfPredator_Type, property=IS_DANGEROUS, value=true, strength=0.8, confidence=0.7, source_type=INNATE)
• AgentMemoryStore: Starts empty.
• CurrentPercepts: Starts empty.
• Position: (x:10, y:5, z:0)
• SensoryCapabilities: Assumed simple radius (e.g., 15 units).

Environmental Setup (Tick 0)

• FoodSource_1 (RedBerryBush_Type): EntityID: FS_01, Position: (x:20, y:5, z:0), sustenance_value: 0.5. (Within H-01’s initial sensory range).
• ThreatSource_1 (WolfPredator_Type): EntityID: TS_01, Position: (x:25, y:15, z:0). (Initially outside H-01’s sensory range, but will move closer).

Step-by-Step Walkthrough

Tick 1 - 2999: Quiescent State & Initial Perception

• SpatialHashUpdateSystem: Updates positions of H-01, FS_01, TS_01.
• NeedManagementSystem: No NeedThresholdReachedEvents fire yet. H-01’s SustenanceNeed is implicitly decaying.
  • Other systems can query GetCurrentNeedValue(H-01, SUSTENANCE, current_tick) which will show a decreasing value.
• PerceptionSystem (runs each tick for H-01):
  • Tick 1: H-01 queries spatial hash. FS_01 is within range (10 units away).
  • CurrentPercepts on H-01 is updated: perceived_entities: [{entity_id: FS_01, entity_type: RedBerryBush_Type, position: (20,5,0)}].
  • Since CurrentPercepts changed from empty, NewEntityInPerceptionRangeEvent(H-01, FS_01, RedBerryBush_Type) is dispatched (assuming subscribers).
• MemoryRecordingSystem (reacts to perception event):
  • Processes NewEntityInPerceptionRangeEvent.
  • Calculates significance (e.g., moderate, as it’s a known food type).
  • Creates MemoryEntry in H-01’s AgentMemoryStore: (timestamp:1, event_type:SAW_ENTITY, involved_entities:[{FS_01, POTENTIAL_FOOD_SOURCE}], location:(20,5,0), significance:0.3).
• Other Systems: No goals active, so Plan/Appraise/Act phases are largely idle for H-01.
• (This loop of perception and minor memory recording continues. TS_01 is still out of range).

Tick 3000: Sustenance Need Becomes Pressing

• Universal Scheduler: Dispatches Event_A (NeedThresholdReachedEvent(H-01, SUSTENANCE, GOAL_TRIGGER)) for H-01.
• NeedManagementSystem:
  • Processes Event_A. Verifies SustenanceNeed (interpolated value is now 0.7 - (0.0001 * 3000) = 0.4).
  • Clears _scheduled_goal_trigger_event_id from H-01’s SustenanceNeed.
  • Dispatches SustenanceGoalCheckRequiredEvent(H-01).
• GoalGenerationSystem (reacts to SustenanceGoalCheckRequiredEvent):
  • H-01 has no active SeekSustenanceGoal.
  • Verifies SustenanceNeed is indeed at/below threshold (0.4).
  • Adds SeekSustenanceGoal component to H-01:
    • priority: 0.6 (calculated: 1.0 - 0.4).
• GoalPrioritizationSystem:
  • H-01 now has one active goal: SeekSustenanceGoal.
  • Designates it as the CurrentActiveGoalFocus for H-01.
• PerceptionSystem:
  • Continues to perceive FS_01. CurrentPercepts is stable regarding FS_01.
  • Scenario Change: Let’s say TS_01 (Wolf) has moved to (x:22, y:10, z:0) and is now also within H-01’s sensory range.
  • CurrentPercepts on H-01 is updated to include TS_01.
  • NewEntityInPerceptionRangeEvent(H-01, TS_01, WolfPredator_Type) is dispatched.
• MemoryRecordingSystem:
  • Processes NewEntityInPerceptionRangeEvent for TS_01.
  • Significance is high due to innate belief about WolfPredator_Type being dangerous.
  • Creates MemoryEntry in AgentMemoryStore: (timestamp:3000, event_type:SAW_ENTITY, involved_entities:[{TS_01, PERCEIVED_THREAT}], location:(22,10,0), significance:0.8, emotional_valence:-0.7).
• GoalGenerationSystem (reacts to perception of TS_01, based on belief):
  • Checks H-01’s AgentBeliefStore: Belief(subject_type=WolfPredator_Type, property=IS_DANGEROUS, value=true, strength=0.8).
  • H-01 has no active SeekSafetyGoal.
  • Adds SeekSafetyGoal component to H-01:
    • priority might be calculated based on perceived danger of Wolf (e.g., very high, 0.9).
    • threat_entity_id: TS_01.
• GoalPrioritizationSystem (runs again due to new goal):
  • H-01 has two active goals:
    • SeekSustenanceGoal (priority: 0.6)
    • SeekSafetyGoal (threat: TS_01, priority: 0.9)
  • SeekSafetyGoal has higher effective priority due to immediate threat.
  • Updates CurrentActiveGoalFocus on H-01 to SeekSafetyGoal.

Tick 3001: Planning and Appraising Safety Actions

• PotentialActionDerivationSystem (for CurrentActiveGoalFocus: SeekSafetyGoal):
  • Strategies for safety: “Flee from threat.”
  • CurrentPercepts shows TS_01 at (22,10,0). H-01 is at (10,5,0).
  • Derives PotentialAction candidates:
    • PA1: MoveTo(Position directly opposite TS_01, e.g., (0,0,0))
    • PA2: MoveTo(Known safe spot if any in memory) - None for H-01 yet.
• ActionAppraisalSystem (for PA1):
  • Invokes AppraisalCriterionEvaluators:
    • EvaluateExpectedNeedSatisfaction (Safety): High (moving away from threat is good for safety).
    • EvaluateRiskToSafety (of the Flee Action): Low initially (if path is clear).
    • EvaluateTimeCost: Moderate.
    • EvaluateImpactOnOtherNeeds: SustenanceNeed will continue to decay; this action doesn’t address it. (Negative impact on Sustenance).
  • Let’s say PA1 gets a high TotalAppraisalScore.
• ActionSelectionSystem:
  • Selects PA1.
  • Adds MoveToTargetAction to H-01:
    • target_position: (0,0,0)
    • target_entity_id: None (fleeing to a point, not an entity)
    • goal_type_driving_action: SEEK_SAFETY
    • intended_on_arrival_interaction: BECOME_SAFE_AT_LOCATION (or similar)
• MovementSystem:
  • Begins processing MoveToTargetAction for H-01. H-01 starts moving towards (0,0,0).
  • Schedules ArrivalAtTargetEvent for a future tick (e.g., Tick 3010).

Tick 3002 - 3009: Executing Flee Action

• MovementSystem: H-01 continues moving.
• NeedManagementSystem: SustenanceNeed continues to decay.
• PerceptionSystem: H-01’s CurrentPercepts updates. TS_01 might also be moving. FS_01 might go out of range. New memories are recorded if perceptions change significantly.

Tick 3010: Flee Action Arrival & Re-evaluation

• Universal Scheduler: Dispatches ArrivalAtTargetEvent(H-01, target_position:(0,0,0), intended_on_arrival_interaction: BECOME_SAFE_AT_LOCATION, goal_type_driving_action: SEEK_SAFETY).
• InteractionInitiationSystem:
  • Processes event. intended_on_arrival_interaction is BECOME_SAFE_AT_LOCATION.
  • This might not add a new interaction component but directly trigger logic within a SafetyStatusUpdateSystem or generate an InteractionOutcomeEvent.
• Let’s assume an InteractionOutcomeEvent is generated: (agent_id:H-01, interaction_type:FLEE_COMPLETED, success:true, details:{new_location:(0,0,0)}).
• MemoryRecordingSystem: Records this successful flee action. MemoryEntry(event_type:COMPLETED_TASK, outcome:POSITIVE, details:{task:FLEE_FROM_TS_01}).
• GoalResolutionSystem:
  • Processes outcome. Is the SeekSafetyGoal (from TS_01) resolved?
  • H-01 needs to check CurrentPercepts. If TS_01 is no longer perceived (or is very far):
    • Removes SeekSafetyGoal from H-01.
    • Generates GoalCompletedEvent(H-01, SEEK_SAFETY).
• BeliefUpdateSystem:
  • The memory of successfully fleeing might reinforce a belief like Belief(self, property=CAN_EVADE_WOLVES, value=true, strength++, confidence++).
• GoalPrioritizationSystem (runs again, as active goal completed):
  • SeekSafetyGoal is gone.
  • SeekSustenanceGoal (priority: now even higher as need decayed further) is the only active goal.
  • Sets CurrentActiveGoalFocus to SeekSustenanceGoal.
• Agent is now at (0,0,0). SustenanceNeed is lower. FS_01 is likely out of perception range.

Tick 3011 onwards: Now Focus on Sustenance

• PotentialActionDerivationSystem (for CurrentActiveGoalFocus: SeekSustenanceGoal):
  • No food in CurrentPercepts.
  • Checks AgentMemoryStore: It has a memory of FS_01 at (20,5,0) from Tick 1.
  • Strategy: “Go to known food and eat.”
  • Derives PotentialAction: PA_Food: MoveTo((20,5,0)) then Eat(FS_01).
  • Strategy: “Search for new food sources.” (This would involve different actions like ExploreArea). Let’s assume for simplicity it only derives PA_Food for now.
• ActionAppraisalSystem (for PA_Food):
  • EvaluateExpectedNeedSatisfaction: High (based on belief about RedBerryBush).
  • EvaluateRiskToSafety: Checks memory/beliefs about location (20,5,0). If TS_01 was last seen moving away from there, risk might be assessed as low to moderate. If TS_01 was seen near there, risk is high. This appraisal is key.
  • EvaluateTimeCost: Calculates travel time to (20,5,0).
• ActionSelectionSystem: Selects PA_Food (assuming appraisal is favorable).
  • Adds MoveToTargetAction to H-01: target_position:(20,5,0), target_entity_id:FS_01, intended_on_arrival_interaction:EAT_FOOD_INTERACTION.
• …and the cycle continues with movement, arrival, eating, and learning from that outcome.

This illustrative example demonstrates:

• How needs trigger goals.
• How perception informs memory and can introduce new, conflicting goals.
• How goal prioritization handles conflicting motivations.
• How plans are derived from goals and knowledge (memory/beliefs).
• How actions are selected via appraisal.
• How actions are executed and their outcomes feed back into memory, beliefs, and goal resolution.

This is, of course, a simplified flow. Many more details, alternative paths, and failure conditions would exist in a full implementation.

This is a long section, as it’s a narrative! Please let me know your thoughts.

• Does the flow make sense and clearly illustrate the system interactions?
• Are there any key system interactions I missed or misrepresented for this scenario?
• Is the level of detail appropriate for an “illustrative example”?

This section is vital for seeing if our abstract system design “works” in a practical sequence.