curiositech SKILL.md — Big Brother Logic: Epistemic Reasoning for Multi-Agent Systems
DECISION POINTS
Primary Decision Tree: Responding to Partial Information States
Given: Agent state S, Goal G, Available actions A
IF satisfiability_check(S, G) == TRUE
├── IF current_knowledge_sufficient(S, G)
│ └── Execute planned action
├── ELSE IF gather_info_viable(S, A)
│ ├── Identify minimum vision set expansion needed
│ ├── Request targeted information from other agents
│ └── Update Kripke model with new information
│ └── Re-evaluate satisfiability_check(S, G)
└── ELSE
└── Reconfigure agent positions/permissions to satisfy G
ELSE IF satisfiability_check(S, G) == FALSE
├── IF goal_relaxation_acceptable()
│ └── Modify G to achievable subset
└── ELSE
└── Abort and escalate to human operator
ELSE IF satisfiability_check(S, G) == UNKNOWN
├── Attempt distributed reasoning with peer agents
├── IF still UNKNOWN after coordination
│ └── Escalate to centralized knowledge computation
└── ELSE proceed with satisfiability branch above
Coordination Failure Recovery Decision Tree
When coordination attempt fails between agents A and B:
IF agents had same information but different actions
├── Check for common knowledge gap
│ ├── Was information publicly announced? → Use public broadcast
│ └── Was announcement verified received? → Add confirmation protocol
└── Check for conflicting vision sets → Resolve perceptual boundaries
IF agents had different information
├── Map each agent's vision set
├── Identify which agent has authoritative view
├── Update uninformed agent's Kripke model
└── Re-attempt coordination
IF coordination succeeds but action fails
├── Epistemic goal was wrong, not coordination
└── Revise knowledge requirements for this task type
FAILURE MODES
1. "False Knowledge" Anti-Pattern
Symptom: Agent claims to "know" something based on incomplete information Detection Rule: If agent acts on belief X but cannot rule out scenarios where ¬X, it has false knowledge Fix: Map agent's vision set; expand perceptual boundaries or add verification step before action
2. "Mutual Knowledge Masquerade"
Symptom: System assumes coordination after broadcasting message to all agents Detection Rule: If coordination fails despite "shared" information, check if agents know that others received the message Fix: Replace broadcast with public announcement protocol; verify common knowledge establishment
3. "Epistemic Goal Drift"
Symptom: Agents follow procedures correctly but system fails to achieve intended outcome Detection Rule: If tasks complete successfully but higher-level goal fails, procedures were specified without epistemic foundation Fix: Rewrite specifications as "Agent X must know Y before doing Z" instead of procedural steps
4. "Centralization Denial"
Symptom: System uses central knowledge computation but claims to be "distributed" Detection Rule: If any single point of failure can corrupt all agents' knowledge states Fix: Either accept centralized architecture with honest trade-off documentation, or redesign for true distributed epistemic reasoning
5. "Vision Set Mismatch"
Symptom: Agent assigned task requiring information outside its perceptual boundaries Detection Rule: If agent cannot distinguish scenarios relevant to its assigned task Fix: Either expand agent's vision set or reassign task to agent with appropriate perceptual access
WORKED EXAMPLES
Example 1: Agent Misconfiguration Recovery
Scenario: Three surveillance agents (A1, A2, A3) monitoring area. A2's camera malfunctions, creating coverage gap.
Initial State: A1 knows east sector clear, A3 knows west sector clear, A2 reports nothing (due to malfunction) Goal: Verify entire area is secure before allowing human entry
Decision Process:
- satisfiability_check(current_state, "area_secure") → UNKNOWN (A2's sector unverified)
- gather_info_viable() → FALSE (A2 cannot provide info)
- Reconfigure: A1 and A3 adjust positions to overlap A2's sector
- New vision sets: A1 covers east + center, A3 covers west + center
- satisfiability_check(new_state, "area_secure") → TRUE
- Execute: Allow human entry
Expert Insight: Novice would wait for A2 to recover or manually check the area. Expert recognizes this as satisfiability problem and solves via reconfiguration.
Example 2: Common Knowledge Coordination
Scenario: Financial trading agents must execute synchronized trades across markets
Initial Attempt: Central system broadcasts "execute trades at 14:30" to all agents Failure: Some agents execute, others don't, causing market position mismatch
Epistemic Analysis:
- Each agent received message (mutual knowledge)
- But agents don't know others received it (no common knowledge)
- Without common knowledge, coordination fails in adversarial environment
Solution:
- Replace broadcast with public announcement requiring confirmation
- Each agent confirms receipt and sees others' confirmations
- Only proceed when common knowledge of "all agents ready" is established
- Result: Perfect synchronization achieved
QUALITY GATES
Task completion requires ALL conditions satisfied:
- Vision set adequacy: Each agent can distinguish all scenarios relevant to its assigned tasks
- Common knowledge depth: For coordination-critical information, all agents know that all agents know (verified to required depth)
- Satisfiability verification: Current epistemic state provably satisfies all knowledge goals before action
- State monotonicity: New information only expands agent knowledge, never contradicts existing knowledge
- Failure mode coverage: System behavior defined for all epistemic failure cases (unknown, false knowledge, coordination failure)
- Centralization trade-off documented: If using centralized computation, failure modes and limitations explicitly stated
- Communication epistemic effect: Each message's impact on agent Kripke models formally specified
- Runtime verification active: Epistemic properties checked before irreversible actions
- Goal achievability confirmed: All epistemic goals are satisfiable given current system constraints
- Boundary condition handling: System behavior defined when goals become unsatisfiable
NOT-FOR BOUNDARIES
This skill should NOT be used for:
- Single-agent reasoning tasks → Use standard planning/decision-making frameworks
- Task coordination with complete shared information → Use workflow management systems
- Performance optimization of existing working systems → Use profiling/optimization tools
- Simple message passing between components → Use standard communication patterns
- Systems where "good enough" coordination is acceptable → Use eventual consistency patterns
Delegate to other skills when:
- Need real-time performance optimization → [performance-optimization-skill]
- Designing human-AI interaction workflows → [human-ai-collaboration-skill]
- Building fault-tolerant distributed systems → [distributed-systems-resilience-skill]
- Implementing security/access control → [multi-agent-security-skill]
Use this skill specifically when:
- Knowledge asymmetry between agents is the core challenge
- Coordination failures occur despite agents having "correct" information
- System must guarantee epistemic properties, not just attempt coordination
- Need formal verification that agents "know enough" before acting