--- license: Apache-2.0 name: bdi-agent-design-mora description: Design patterns for BDI agents using the MORA methodology for practical multi-agent system development category: Research & Academic tags: - bdi - agents - design-patterns - mora - architecture --- # BDI Agent Design (Móra et al.) **Skill ID**: `bdi-agent-design-mora` **Version**: 1.0 **Author**: Based on "BDI Models and Systems: Reducing the Gap" by Móra, Lopes, Viccari, and Coelho **Activation Triggers**: BDI architecture, agent systems, intention reasoning, belief-desire-intention, operational semantics, agent deliberation, commitment mechanisms, rational agents ## Description Design and implement rational agent systems using the Beliefs-Desires-Intentions (BDI) paradigm with executable semantics. This skill bridges the theory-practice gap by using Extended Logic Programming with paraconsistent semantics as both formal specification AND reasoning engine. ## Decision Points ### Core Architecture Choice ``` IF building theoretical specification OR formal verification required ├─ Use axiomatic modal/temporal BDI logics (Cohen & Levesque, Rao & Georgeff) └─ Accept theory-implementation gap IF building executable agent system ├─ Use Extended Logic Programming with operational semantics └─ Formal specification IS the reasoning engine ``` ### Intention Revision Triggers ``` IF action completes/fails ├─ Remove completed intentions from commitment set ├─ Check if failure makes other intentions impossible └─ Filter satisfied desires from candidate pool IF deadline reached ├─ Remove expired intentions ├─ Re-evaluate previously delayed desires └─ Trigger replanning for dependent actions IF belief-intention contradiction detected ├─ IF abduction can find missing preconditions → revise beliefs └─ IF intention truly impossible → abandon intention IF higher-priority desire becomes feasible ├─ IF conflicts with current intentions → trigger deliberation └─ IF compatible → adopt without disrupting commitments IF no trigger condition met └─ Maintain current intentions (commitment persistence) ``` ### Negation Strategy Selection ``` IF representing "agent actively believes/desires X is false" └─ Use explicit negation: ¬P IF querying "is there evidence for X?" └─ Use negation-by-failure: not P IF detecting conflicts between mental states ├─ Need explicit negation for: desire(P) ∧ desire(¬P) └─ Negation-by-failure cannot detect this contradiction ``` ### Conflict Resolution Approach ``` IF desires directly contradict (P ∧ ¬P) ├─ Apply priority ordering └─ Keep higher-priority desire, remove lower IF desires have incompatible resource requirements ├─ Use abduction to test joint feasibility ├─ IF multiple consistent subsets exist → apply maximality preference └─ IF no consistent subset → escalate to user/higher-level goal IF paraconsistent contradiction detected ├─ Trigger minimal revision procedure ├─ Restore consistency through preference-guided removal └─ Use contradiction as deliberation input, not error condition ``` ## Failure Modes ### **Modal Logic Without Proof Procedures** **Detection**: Elegant BDI specifications exist but implementation uses ad-hoc Java/Python data structures with no resemblance to specification **Root Cause**: Choosing specification formalisms that cannot execute **Fix**: Use formalisms where specification IS executable (Extended Logic Programming) or commit to mechanized modal logic with runtime theorem proving ### **Negation Conflation** **Detection**: System uses only `not P` for both "unknown" and "actively false"; cannot represent negative intentions like "intend NOT to interrupt user" **Root Cause**: Treating negation-by-failure as sufficient for all negative information **Fix**: Use explicit negation `¬P` for affirmative negative knowledge; reserve `not P` for closed-world queries ### **Perpetual Re-deliberation** **Detection**: Agent recalculates optimal intentions every cycle; never executes plans longer than one decision cycle; high CPU usage in deliberation **Root Cause**: No commitment mechanism; treating all desires as immediate commands **Fix**: Implement trigger-based revision with explicit commitment constraints; intentions persist between triggers ### **Contradiction Crashes** **Detection**: System enters undefined state or throws exceptions when desires conflict; requires pre-filtering desires for consistency **Root Cause**: Classical logic semantics where contradictions make everything provable **Fix**: Use paraconsistent semantics (WFSX) where contradictions are detectable signals triggering deliberation ### **Combinatorial Preference Explosion** **Detection**: System generates all possible consistent desire subsets then applies preference; exponential slowdown with desire set size **Root Cause**: Treating preference as post-processing filter rather than search guidance **Fix**: Integrate preference into revision procedure; guide search toward preferred revisions without enumerating all possibilities ## Worked Examples ### Example 1: Belief-Intention Contradiction Resolution **Scenario**: Household robot intends to `serve_coffee` but discovers `coffee_maker_broken`. ```prolog % Initial state belief(coffee_maker_broken). intention(serve_coffee). action_precondition(serve_coffee, working_coffee_maker). % Contradiction detection (paraconsistent semantics) contradiction :- intention(serve_coffee), belief(coffee_maker_broken), action_precondition(serve_coffee, working_coffee_maker), not belief(working_coffee_maker). % Abductive feasibility check missing_precondition(X) :- intention(A), action_precondition(A, X), not belief(X), not belief(¬X). % Revision options: % Option 1: Abandon intention revised_intentions_1([]) :- contradiction. % Option 2: Abductive belief revision (find alternative) revised_beliefs_2([belief(use_instant_coffee), belief(working_instant_dispenser)]) :- contradiction, alternative_action(serve_coffee, use_instant_coffee), abducible(working_instant_dispenser). ``` **Decision Process**: 1. Contradiction detected → trigger deliberation 2. Check abductive alternatives → instant coffee possible 3. Preference evaluation → satisfying desire preferred over abandoning 4. Result: Revise beliefs to include instant coffee option, maintain serve_coffee intention **Novice Miss**: Would abandon intention immediately without checking alternatives **Expert Catch**: Uses abduction to find feasible alternative means to same end ### Example 2: Competing Desire Deliberation **Scenario**: Personal assistant agent with conflicting scheduling desires. ```prolog % Competing desires desire(schedule_meeting(client_A, 2pm)). desire(¬schedule_meeting(client_A, 2pm)). % Explicit negation - active aversion desire(schedule_workout(2pm)). % Priority information priority(schedule_meeting(client_A, 2pm), 8). priority(schedule_workout(2pm), 6). % Resource constraints conflicts(schedule_meeting(client_A, 2pm), schedule_workout(2pm)) :- same_time_slot(2pm, 2pm). % Revision procedure deliberate_intentions(Result) :- find_contradictions(Conflicts), resolve_by_priority(Conflicts, Resolved), check_resource_conflicts(Resolved, Final), Result = Final. % Paraconsistent detection find_contradictions([(desire(P), desire(¬P)) | Rest]) :- desire(P), desire(¬P), find_contradictions(Rest). % Priority-based resolution resolve_by_priority([(desire(P), desire(¬P))], [desire(P)]) :- priority(P, X), priority(¬P, Y), X > Y. ``` **Decision Process**: 1. Explicit negation enables detecting desire(schedule_meeting) ∧ desire(¬schedule_meeting) 2. Priority resolution: schedule_meeting (priority 8) beats ¬schedule_meeting 3. Resource check: schedule_meeting conflicts with schedule_workout 4. Final: adopt schedule_meeting(client_A, 2pm), reject others **Novice Miss**: Would use only negation-by-failure, missing the explicit aversion **Expert Catch**: Recognizes explicit negative desires as different from mere absence ### Example 3: Commitment Persistence Under Temptation **Scenario**: Study assistant maintains focus intention despite social media desires. ```prolog % Committed intentions (established through previous deliberation) committed_intention(study_mathematics, until(exam_complete)). committed_intention(¬use_social_media, until(study_session_end)). % New desires arise desire(check_facebook). desire(browse_instagram). % Commitment constraint check viable_intention(X) :- desire(X), \+ conflicts_with_commitment(X). conflicts_with_commitment(X) :- committed_intention(¬X, Until), \+ condition_met(Until). % No trigger conditions met trigger_deliberation :- (action_completed(_) ; deadline_reached(_) ; impossibility_detected(_)). % Result: No deliberation triggered, maintain commitments current_intentions(Result) :- \+ trigger_deliberation, findall(I, committed_intention(I, _), Result). ``` **Decision Process**: 1. New desires arise but no trigger condition met 2. Commitment constraints block social media desires 3. Study intentions persist without re-evaluation 4. Result: maintain current commitments, ignore tempting desires **Novice Miss**: Would re-evaluate all desires, breaking commitment **Expert Catch**: Commitment means NOT reconsidering unless specific triggers fire ## Quality Gates - [ ] Contradiction detection implemented using paraconsistent semantics (can detect P ∧ ¬P without system failure) - [ ] Explicit negation distinguished from negation-by-failure (can represent both "actively false" and "unknown") - [ ] Commitment persistence mechanism prevents arbitrary re-deliberation (intentions persist unless trigger conditions met) - [ ] Abductive feasibility checking available for intention adoption (can detect missing preconditions before commitment) - [ ] Trigger conditions explicitly specified for deliberation (action completion, deadlines, impossibility, higher-priority opportunities) - [ ] Preference ordering integrated into revision procedure (guides search, doesn't post-filter all options) - [ ] Belief and intention representations unified in same logical framework (enables integrated consistency checking) - [ ] Minimal revision procedures prefer smaller changes to mental state (stability over arbitrary change) - [ ] Resource conflict detection prevents incompatible simultaneous intentions (time, physical, logical constraints) - [ ] Deliberation termination criteria prevent infinite reconsideration loops (commitment or explicit failure) ## NOT-FOR Boundaries **This skill is NOT for**: - **Pure theorem proving systems**: For formal verification without execution, use modal BDI logics instead - **Reactive architectures**: For stimulus-response systems without deliberation, use `reactive-agent-patterns` instead - **Multi-agent negotiation**: For inter-agent protocols and communication, use `agent-communication-protocols` instead - **Machine learning integration**: For learning-based goal adaptation, use `reinforcement-learning-agents` instead - **Real-time hard constraints**: For systems where deliberation latency is unacceptable, use `real-time-agent-scheduling` instead - **Distributed consensus**: For coordinating intentions across multiple agents, use `distributed-agent-coordination` instead **Delegate to other skills when**: - Agent needs to learn new goals → `goal-learning-systems` - Multiple agents must coordinate plans → `multi-agent-planning` - Environment is fully observable and deterministic → `classical-planning-agents` - Real-time performance critical → `anytime-reasoning-agents`