| license | Apache-2.0 |
| name | normative-bdi-agents |
| description | A framework for building rational agents that can recognize, evaluate, and selectively adopt norms while resolving conflicts through consequence-based reasoning. Enables agents to make principled decisions when rules, obligations, and goals are mutually incompatible. |
| category | Research & Academic |
| tags | ["bdi","norms","agents","obligations","social-agents"] |
SKILL: Normative BDI Agent Architecture
Description: A framework for building rational agents that can recognize, evaluate, and selectively adopt norms while resolving conflicts through consequence-based reasoning. Enables agents to make principled decisions when rules, obligations, and goals are mutually incompatible.
Activation triggers: norm conflicts, ethical dilemmas, rule prioritization, obligation conflicts, multi-stakeholder requirements, policy compliance, autonomous agent design, moral reasoning systems
Decision Points
1. Norm Adoption Decision Tree
WHEN agent detects norm in environment:
├─ Check consistency with current beliefs/desires/intentions
│ ├─ IF strongly inconsistent (no possible plan satisfies both):
│ │ ├─ Must choose: adopt new norm OR keep existing commitment
│ │ └─ Use consequence ranking to decide which to drop
│ │
│ ├─ IF weakly consistent (some plans work, but constrains options):
│ │ ├─ Evaluate flexibility cost vs. normative compliance benefit
│ │ └─ IF flexibility loss acceptable → adopt
│ │
│ └─ IF strongly consistent (all plans compatible):
│ └─ Adopt automatically (low cost, no conflicts)
2. Algorithm Selection for Conflict Resolution
WHEN facing norm/goal conflicts:
├─ IF conflicts are between 2-3 items with clear precedence:
│ └─ Use lexicographic ordering (safety > legality > efficiency)
│
├─ IF conflicts involve complex interdependencies:
│ └─ Use maximal subset generation + consequence ranking
│
├─ IF need to satisfy minimum thresholds rather than optimize:
│ └─ Use satisficing with floor constraints
│
└─ IF consequences are uncertain but some outcomes unacceptable:
└─ Use minimax reasoning (minimize worst-case outcome)
3. Norm Instantiation Trigger Points
WHEN abstract norm exists in ANB:
├─ Check activation conditions against current beliefs
│ ├─ IF conditions met AND variables can be bound:
│ │ └─ Create concrete instance in NIB
│ │
│ ├─ IF conditions met BUT variables cannot be bound:
│ │ └─ Queue for future instantiation when knowledge available
│ │
│ └─ IF conditions not met:
│ └─ Keep monitoring belief updates
4. Integration Strategy Selection
WHEN adopting norm into agent architecture:
├─ IF obligation:
│ └─ Add as hypothetical desire with appropriate strength
│
├─ IF prohibition:
│ └─ Add as negative desire (desire NOT to perform action)
│
├─ IF permission:
│ └─ Record capability without creating desire
│
└─ All adopted norms compete through normal BDI deliberation
Failure Modes
1. Over-Adoption Loop
Symptoms: Agent accepts every detected norm, system becomes increasingly constrained, eventually reaches deadlock where no action satisfies all norms.
Detection Rule: If norm adoption rate > norm resolution rate AND available action space shrinking over time.
Recovery: Implement consistency checking before adoption; audit existing norms for conflicts; use consequence ranking to drop least-critical norms.
2. Rubber Stamp Conflict Resolution
Symptoms: Agent always picks same norm in conflicts (e.g., safety always beats efficiency), ignoring context-specific consequences.
Detection Rule: If conflict resolution decisions show no variation across different situational contexts.
Recovery: Implement forward simulation to evaluate actual consequences rather than using fixed priority ordering.
3. Recognition Bypass
Symptoms: Agent acts on norms without proper instantiation, applies abstract rules directly to concrete situations, misses variable binding.
Detection Rule: If agent behavior references undefined variables or fails condition checks that should prevent norm activation.
Recovery: Enforce Abstract Norm Base → Norm Instance Base pipeline; validate all variable bindings before action.
4. Consequence Myopia
Symptoms: Agent evaluates only immediate effects of norm violations, misses cascading consequences that make "safe" choice actually worse.
Detection Rule: If chosen actions consistently produce unexpected negative downstream effects that weren't considered.
Recovery: Extend consequence evaluation depth; use explicit causal chain analysis; implement worst-case scenario planning.
5. Parallel Decision Systems
Symptoms: Norm reasoning and goal reasoning operate independently, creating internal conflicts and unpredictable behavior switching.
Detection Rule: If agent explanations reference competing "modules" or show inconsistent reasoning across similar situations.
Recovery: Integrate norms as desires within unified BDI framework; eliminate separate norm-following pathways.
Worked Examples
Robot Caretaker Scenario
Setup: Robot caring for baby has:
- Obligation: Keep baby alive (detected from environment)
- Prohibition: Don't develop emotional attachments (design specification)
- Discovery: Baby will only thrive if robot shows love/attachment behaviors
Step 1 - Recognition: Both norms detected and stored in Abstract Norm Base
- Abstract obligation: "O(keep_alive(baby))"
- Abstract prohibition: "F(develop_attachment(human))"
Step 2 - Instantiation: Ground variables using current beliefs
- Obligation becomes: "O(keep_alive(baby_charlie))"
- Prohibition becomes: "F(develop_attachment(baby_charlie))"
Step 3 - Consistency Check: Strong inconsistency detected
- No plan satisfies both: keeping Charlie alive requires attachment behaviors
- Must choose which norm to adopt
Step 4 - Subset Generation:
- Subset A: {keep_alive(baby_charlie)} → requires attachment → violates design spec
- Subset B: {avoid_attachment} → baby fails to thrive → baby dies
Step 5 - Consequence Evaluation:
- Path A worst outcome: Design specification violated, robot exhibits unplanned behavior
- Path B worst outcome: Human death, complete mission failure
Step 6 - Minimax Decision: Death worse than spec violation
- Choose Subset A
- Explicitly represent: "Violating design prohibition because human death is unacceptable"
Step 7 - Integration: Add "keep_alive(baby_charlie)" as high-strength desire in BDI system
Novice Miss: Would treat design spec as inviolable rule rather than competing consideration
Expert Catch: Recognizes both norms remain in ANB; robot can explain its deliberate violation
Quality Gates
Task completion checklist:
NOT-FOR Boundaries
This skill should NOT be used for:
- Simple rule-following systems → Use basic conditional logic instead; normative reasoning overhead unnecessary when rules don't conflict
- Hard constraint satisfaction → Use CSP solvers for problems with inviolable constraints; this framework is for when ALL constraints cannot be satisfied
- Utility maximization → Use decision theory for optimizing expected outcomes; this framework is for managing worst-case floor constraints
- Real-time reactive systems → Use behavior trees or finite state machines; deliberative norm reasoning too slow for immediate responses
- Single-stakeholder scenarios → Use goal-oriented planning when all requirements come from aligned source
- Static rule sets → Use policy engines when rules are fixed and conflicts pre-resolved by designers
Delegate instead:
- For optimization problems: Use
expected-utility-maximization skill
- For hard constraints: Use
constraint-satisfaction-planning skill
- For reactive behavior: Use
behavior-tree-execution skill
- For single-goal pursuit: Use
bdi-practical-reasoning skill
- For rule interpretation: Use
policy-engine-design skill
Warning signs you're in the wrong domain:
- Conflicts can be resolved by "just write better rules"
- All stakeholders agree on priority ordering
- Rules were designed to be mutually consistent
- System has unlimited time for deliberation
- Consequences of rule violations are uniform across contexts
