Cognitive Computation Research
AI with Symbolic Empathy: Shannon-Neumann Insight Guided Logic
This research introduces an Artificial Intelligence system with Symbolic Empathy, designed to cooperatively align a person's cognitive state with their ideal trajectory. It leverages a context-sensitive, non-monotonic logic implemented through a five-stage Hierarchical Finite-State Machine, driven by a novel Shannon-von Neumann insight gain for self-supervised abductive learning.
Executive Impact & Key Advantages
Our innovative AI framework offers unprecedented transparency and goal alignment, ensuring effective and ethical human-AI collaboration.
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Avg. Uncertainty Reduction
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Total Insight Gain
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Core AI Principles Met (I-AI, X-AI, T-AI)
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Goal-Aligned Human-AI Interaction
Deep Analysis & Enterprise Applications
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Framework Overview
Core Logic: LA
Insight Metric: ISN
Learning & Accountability
A New Paradigm for Empathetic AI
Our framework introduces an Artificial Intelligence with Symbolic Empathy, where an agent cooperatively aligns a person's cognitive state with an ideal trajectory. This alignment is guided by a context-sensitive, non-monotonic logic and operationalized through a five-stage Hierarchical Finite-State Machine (HFSM). This ensures the AI identifies patterns, explores interventions, plans for diagnosis, and reasons through abductive hypothesis evaluation.
This system ensures interpretability (I-AI), explainability (X-AI), and trustworthiness (T-AI) via explicit fact-rule tracing, providing a transparent and auditable human-AI interaction.
Context-Sensitive AI Logic (LA)
The agent's reasoning is governed by LA, a context-sensitive, goal-directed, non-monotonic logic. It's operationalized through a five-stage Hierarchical Finite-State Machine (HFSM):
- Identify: Transforms raw inputs into structured facts.
- Classify: Applies symbolic rules to identify significant patterns.
- Explore: Proposes candidate interventions for inconsistencies.
- Plan: Diagnoses misalignment based on cognitive-causal ontology.
- Reason: Hypothesizes causal paths and evaluates them with Shannon-von Neumann insight gain.
This framework enables computation-ally grounded symbolic functional consciousness, with insight, planning, and meaning co-emerging from structured symbolic interaction.
Shannon-von Neumann Insight Gain (ISN)
The core of our reasoning is the Shannon-von Neumann insight gain (ISN), a metric that combines entropy reduction with goal-relevant utility. It is defined as ISN(γi) = [H(Bp(t)) – H(Bp(t + 1))] · U(γi), where H measures epistemic uncertainty and U(γi) reflects the utility of hypothesis γi for long-term symbolic coherence and psychological wellbeing.
This multiplicative form ensures that candidate paths are prioritized only when they are both epistemically informative and pragmatically useful, preventing the selection of low-value or weakly supported hypotheses. It enables self-supervised abductive learning.
Goal-Directed Self-Supervised Learning & Accountability
The logic LA embeds a form of goal-directed self-supervised learning, continuously refining its internal belief distribution over causal hypotheses. Agent A autonomously generates candidate hypotheses, predicts consequences, and selects observations or questions to maximize ISN(γi).
This leads to: self-supervised (learning from self-generated hypotheses), goal-aligned (guided by utility), non-monotonic and adaptive (revisable via context), and transparent (through symbolic reasoning steps interpretable by humans). Every step is logged, guaranteeing I-AI, X-AI, and T-AI.
Enterprise Process Flow: AI Reasoning Lifecycle
Identify (Fact Structuring)
→
Classify (Pattern Extraction)
→
Explore (Intervention Generation)
→
Plan (Misalignment Diagnosis)
→
Reason (Abductive Hypothesis Evaluation)
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Total Shannon-von Neumann Insight Gain (bits)
This metric quantifies the combined epistemic uncertainty reduction and goal-relevant utility for proposed interventions, ensuring optimal, context-aware decision-making.
| Feature | Shannon-von Neumann Insight Gain (ISN) | Kullback-Leibler (KL) Divergence |
|---|---|---|
| Goal-Directed Utility | Integrates U(γ) for pragmatic effectiveness, enhancing decision quality and planning. | Lacks mechanism for incorporating goal-directed utility, detached from action. |
| Epistemic Uncertainty | Minimizes Shannon entropy H(Ω), reducing uncertainty over candidate causes. | Measures relative information loss (dissimilarities between distributions). |
| Context Awareness | Incorporates C(t) for non-monotonic inference, handling exceptions and reversals. | Asymmetric and detached from action utility. |
| Application Focus | Prioritizes hypotheses that are both informative and useful for human-aligned AI. | Primarily quantifies statistical dissimilarity between probability distributions. |
Case Study: AI-Guided Self-Identity Alignment
This case illustrates how Agent A helps person P align their cognitive state with their ideal trajectory. By analyzing maladaptive patterns like 'low assertiveness' or 'procrastination', the AI infers latent causes ('unclear values') and proposes interventions to improve self-awareness, self-confidence, and sense of identity.
Key Takeaways:
- Identifies maladaptive patterns (e.g., procrastination, low motivation) from observations.
- Abductively infers root causes, such as 'unclear values', using domain knowledge.
- Deduces impairments (e.g., weak self-awareness, low sense of identity) from inferred causes.
- Proposes targeted interventions to address upstream cognitive deficits, guiding P towards ideal self-state.
Calculate Your Potential ROI with Empathetic AI
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Estimated Annual Savings
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Annual Hours Reclaimed
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Our Future-Forward Implementation Roadmap
Building on this foundational research, our continuous development focuses on expanding the capabilities and applications of empathetic AI.
Ontology Enrichment
Developing an empirically validated causal ontology (ΓΩ) encompassing cognitive capacities, emotional states, mindsets, and interventions.
Enhanced Theory-of-Mind Modeling
Extending AI's theory-of-mind capabilities to capture latent goals, emotional priors, and adaptive user preferences for deeper understanding.
Rule Base Expansion
Systematic expansion of the rule base in LA to support a broader, context-sensitive range of abductive, deductive, and dialogical strategies.
Formal Development of ISN
Further formalizing and empirically evaluating the Shannon-Neumann insight gain measure across cognitive simulation tasks.
Neuro-Symbolic Integration
Fusing symbolic reasoning with neural embedding spaces to support perceptual grounding, concept formation, and scalable learning.
Quantum-Causal Extensions
Exploring quantum cognitive models to represent entangled cognitive states and subjective uncertainty using quantum-inspired logic.
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