World-Aware Planning Narrative Enhancement (WAP)

• World-Aware Planning Narrative Enhancement (WAP) comprises methods that generate causally coherent narratives grounded in dynamic world states, integrating explicit causal structure and character intentionality.
• WAP uses techniques like the Intent-based Partial Order Causal Link (IPOCL) planner to build plans through iterative refinement, explicitly representing character goals and motivations alongside plot progression.
• This approach improves narrative comprehension and believability, enabling adaptive, explainable agent behavior in applications like interactive storytelling, games, and training simulations.

World-Aware Planning Narrative Enhancement (WAP) is a set of methodologies and algorithmic strategies designed to produce narratives—whether as stories, plans, or agent behaviors—that are both causally coherent and deeply grounded in the state and evolution of a simulated or real world. Across classical symbolic planners, ontology-driven agents, LLM-based systems, and hybrid approaches, WAP ensures that generated sequences of actions or events remain logically consistent, character-motivated, and responsive to both environmental dynamics and user/agent interventions.

1. Foundations: Integrating Causal Structure and Character Intentionality

The core principle underpinning WAP is the dual requirement for causal soundness and character believability in narrative generation. As formalized in the Intent-based Partial Order Causal Link (IPOCL) planner, a narrative plan is constructed not just as a sequence of causally linked actions $(S, B, O, L)$ but is augmented by frames of commitment $C$:

$(S, B, O, L, C)$

• $S$: Steps/actions
• $B$: Binding constraints
• $O$: Ordering constraints
• $L$: Causal links
• $C$: Frames of commitment (structures encapsulating character goals, motivations, and intentional action groupings)

A frame of commitment is itself a tuple:

$(S', P, a, g_a, s_f)$

• $S' \subset S$: Steps performed by character $a$ in pursuit of goal $g_a$
• $s_f$: Step achieving $g_a$

Every non-happening action must be associated with such a frame, and each frame must be motivated via prior steps in the plan, ensuring perceived intentionality.

2. Refinement Search and Narrative Plan Construction

The IPOCL methodology applies iterative refinement search, where narrative plans are constructed by:

• Identifying plan flaws (unsatisfied preconditions, open motivation flaws, intent flaws, or threats)
• Incrementally refining the plan using least-commitment principles—deferring irreversible decisions until necessary
• Non-deterministically selecting and repairing flaws, thus exploring a space of possible plans
• Integrating both standard causal link management and novel intentionality-focused repair operators

The plan is complete only if:

1. All preconditions are established.
2. All threats (including intentional threats) are resolved.
3. All non-happening actions are assigned to frames of commitment with established motivation.

This process produces narratives where plot progression and character actions are both explainable in audience terms. For example, if Aladdin gives the magic lamp to Jafar, IPOCL ensures prior steps establish Aladdin's motivation—such as loyalty or coercion—so the audience can infer and believe his intentions.

3. Decoupling Author and Character Goals: Narrative World Awareness

A central tenet of world-aware narrative planning is the decoupling of authorial and character goals:

• Author goals: The ultimate state(s) or outcomes desired by the narrative system (e.g., the villain is defeated).
• Character goals: Inferred, constructed, and supported by plan structure to rationalize character actions (e.g., revenge, rescue).

By explicitly representing both, the system can generate narratives that are not only successful in achieving authorial outcomes but also maintain audience suspension of disbelief, presenting characters as intentional and psychologically plausible agents.

Through world-aware planning, the system can react to evolving world states or user interventions: new environmental conditions may trigger new motivation frames or invalidate existing goals, necessitating re-planning or adaptation.

4. Evaluation: Audience Comprehension, Coherence, and Practical Performance

Empirical evaluation demonstrates that WAP-enhanced planners (e.g., IPOCL) yield narratives with superior:

• Comprehensibility: Users have higher accuracy in answering questions about character motivations (IPOCL mean "good" question rating: 3.20 vs. POCL: 2.99).
• Believability: Narratives preserve logical and psychological plausibility, as measured both by automatic (QUEST model) and human evaluation.

This planning rigor translates to practical domains, including:

• Interactive digital storytelling and games, where player interventions trigger world changes and require real-time narrative adaptation.
• Simulation-based training and education, where scenario progression must remain explainable and coherent in the face of learner decisions or unexpected events.

5. Extensions and Integration with Dynamic, Complex Worlds

The world-aware principles of IPOCL and related planners can be extended to support richer, dynamic narrative environments:

• Character–World Coupling: Frames of commitment and intentional flaws allow narrative generators to rationalize character responses to world changes (e.g., disasters, resource depletion).
• Explainable Agency: The explicit structure produced by frames of commitment and open motivation flaws enables runtime querying, debugging, and explanation of character behaviors for players or authors.
• Adaptive and Multi-Agent Worlds: WAP frameworks accommodate multiple agents/factions with independent and shifting goals, supporting emergent, robust storytelling in complex simulations.

Potential extensions include:

• World-aware heuristics: Planners may leverage domain knowledge about environmental dynamics or typical agent reactions.
• Incomplete Commitment Frames: Allowing characters to plan and fail (e.g., foiled attempts) introduces dramatic tension and better models real-world and narrative complexity.
• Integration of External World Events: Planners may continuously ingest environmental changes, triggering new planning cycles and adjustments to both plot and character arcs.

6. Formalism and Algorithmic Summary

The IPOCL planning problem is defined as:

$(I, A, G, \mathcal{A})$

with:

• $I$: Initial world state
• $A$: Set of agents
• $G$: Goal situation(s)
• $\mathcal{A}$: Domain action schemas

The high-level algorithm operates as:

1. If the plan is consistent and flaw-free, return plan.
2. Select a flaw (open condition, open motivation, intent flaw).
3. Refine the plan to address the flaw (create/extend frames, assign motivations, resolve threats).
4. Recurse until a complete, consistent plan is produced or failure is detected.

7. Implications and Applications for World-Aware Interactive Systems

World-Aware Planning Narrative Enhancement is broadly applicable across fields where plans and stories must both drive toward predefined goals and adapt to evolving world dynamics. Key implications include:

• Robustness to Environmental Change: Supports dynamic narrative systems that can react to user intervention, unanticipated world events, or environmental changes by updating motivations and re-planning accordingly.
• Multi-Agent and Emergent Narrative Support: Accommodates large, interactive environments where multiple characters or factions pursue independent, sometimes conflicting objectives.
• Explainable and Debuggable AI: Provides structures for runtime explanation, supporting transparency and understanding in interactive entertainment, training, or simulation.

Aspect	IPOCL/WAP Principle	Example Extension
Causal Coherence	Iterative refinement, causal links	Dynamic event integration
Intentionality	Frames of commitment, motivation	World events justify frames
Audience Comprehension	Explicit motivation, plan structure	Supports explainable agents
Adaptivity	Least-commitment refinement	Runtime world-state re-planning

In summary, World-Aware Planning Narrative Enhancement leverages explicit causal and intentional structures to generate, adapt, and explain narratives that are logically, psychologically, and contextually plausible within dynamically evolving environments. This foundation enables robust, interactive systems capable of producing engaging, coherent, and explainable stories in real-world and simulated domains.