Action Knowledge Generation

• Action Knowledge Generation is a computational framework that integrates declarative, procedural, and conditional knowledge through active perception and active inference.
• It employs probabilistic generative models and unsupervised Bayesian techniques to continuously update action policies and sensory representations.
• The framework advances beyond traditional semantic networks by enabling dynamic, context-dependent action selection through free energy minimization.

Action knowledge generation encompasses the computational mechanisms, mathematical frameworks, and system architectures designed to produce, represent, and utilize explicit, manipulable knowledge about actions, their structure, and their appropriate application in intelligent agents and artificial cognitive systems. Central to recent advances is the move beyond static semantic networks for declarative facts, toward models that support the emergence, learning, and updating of procedural (“how to”) and conditional (“when/why to act”) knowledge through active engagement with the environment. One of the most comprehensive frameworks to date for integrating these modalities is based on the free energy principle (FEP), yielding an unsupervised, probabilistically grounded generative approach capable of flexibly generating, updating, and deploying action knowledge via an action-perception cycle (Ghasimi et al., 25 Jan 2025).

1. Types of Knowledge and Their Generation

The framework differentiates and operationalizes three main types of knowledge:

1. Declarative Knowledge: Encodes factual information about the environment ("what is"), generated via perception and inference from observed stimuli. This aligns with traditional semantic networks.
2. Procedural Knowledge: Specifies how to perform actions or tasks ("how to"), emerging from active inference—selecting and executing action policies that minimize prediction error or surprise.
3. Conditional Knowledge: Encapsulates rules for context-dependent application of declarative and procedural knowledge ("when/why/how to use"), resulting from integrated cycles of action and perception with uncertainty-aware policy selection.

These types are not limited to static structures but are algorithmically produced and updated through continuous interaction with a probabilistic world model.

2. Mathematical Formalism: Generative Models and Free Energy Minimization

The model represents the agent’s epistemic state by latent concepts $S=\{s_1,\ldots,s_n\}$ and observed stimuli $R=\{r_1,\ldots,r_m\}$, linked by an "A-matrix" that encodes the generative process for stimulus-concept associations. Knowledge is generated and updated using mathematical objectives informed by information theory and variational Bayesian principles:

• Mutual Information for Knowledge Transfer:

$$I(S, R) = \sum_{i=1}^n \sum_{j=1}^m p(s_i, r_j) \log \frac{p(s_i, r_j)}{p(s_i) p(r_j)}$$

This captures how much knowing the concept reduces uncertainty about the stimuli, guiding communication and conceptual learning.

• Energy Function for Joint Objectives:

$\Xi(\lambda) = -\lambda I(S, R) + (1-\lambda) H(S)$

where $H(S)$ is concepts' entropy and $\lambda$ balances informativeness against representational compactness.

• Free Energy Principle (FEP):

$$F = D_{KL}[q(\theta|\phi_t) \| p(\theta|y)] - \ln p(y)$$

Free energy $F$ bounds the surprise of observations. Minimization drives both perceptual inference (updating beliefs) and active action selection (acting to realize preferred observations).

• Bayesian Generative Model Structure:

$$p(o, s, \pi) = p(s_1)p(\pi) \prod_{\tau} p(o_\tau|s_\tau) p(s_{\tau}|s_{\tau-1}, \pi)$$

Here, $\pi$ indexes policies (sequences of actions), $s_\tau$ denotes hidden states/concepts at time $\tau$, and $o_\tau$ are observations.

• Policy Selection via Expected Free Energy:

After computing expected free energy $G(\pi)$ over candidate policies, agents select the policy that minimizes $G$, favoring both epistemic (information-seeking) and pragmatic (goal-driven) objectives.

3. Action-Perception Cycle and Active Inference

Action knowledge is generated and expanded through the action-perception cycle, comprising:

• Loop I: Perception (Declarative):

The agent passively updates internal beliefs about the world by inferring hidden causes from sensory input, enriching its declarative knowledge base.

• Loop II: Active Inference (Procedural/Conditional):

When observation violates the agent's predictions (prediction error remains high), it seeks to minimize future surprise by actively choosing and executing actions (policies) to elicit more informative data or bring the environment into alignment with expectations. Through repeated cycles, the agent learns “how to”—establishing procedural skills—and “when/why to” act, contextualizing rules for action application.

• Knowledge Expansion and Adaptation:

The system expands its set of concepts, actions, and policies flexibly as new data is encountered, employing Bayesian nonparametrics (e.g., Dirichlet processes) for unsupervised model growth.

4. Representation, Updating, and Use of Action Knowledge

Action knowledge is not only stored as static associations but is encapsulated in policy distributions and probabilistic mappings that are continuously updated:

• Declarative knowledge is stored as the A-matrix and related mappings between latent concepts and stimuli.
• Procedural knowledge is encoded as learned sequences of action policies, represented and selected via policy priors and transition matrices ($B$ matrices), refined by past experience.
• Conditional knowledge is stored and retrieved through policy selection mechanisms that integrate context-dependent cues and uncertainty, enabling contextually precise action deployment.

Model updating occurs through unsupervised Bayesian learning: new, unexplained stimuli combinations trigger hypothesis formation (new concepts/policies), and experience-driven updates to probabilistic mappings.

5. Empirical and Theoretical Significance

This approach unifies the mechanistic generation of all three types of knowledge under a single mathematical and algorithmic umbrella. It:

• Surpasses classical semantic networks by supporting procedural and conditional knowledge, not just declarative.
• Aligns with contemporary cognitive neuroscience, rooting knowledge growth in active, goal-driven behavior as well as passive observation.
• Enables fully unsupervised, self-organizing model growth and adaptation via information-theoretic and variational principles.
• Generates and encodes action knowledge as policy distributions, explicitly linking action selection to conceptual and contextual inference.

Table: Summary of Knowledge Generation Paths

Knowledge Type	Mechanism	Loop Used	Action Involved
Declarative	Bayesian perception	I	No
Procedural	Active inference	I and II	Yes
Conditional	Policy/context integration	Integration of I and II	Yes

6. Computational Implementation Details

The implementation leverages:

• Matrix representations ($A$, $B$, $C$, $D$) for generative and transition models.
• Variational inference algorithms to minimize free energy over belief and action spaces.
• Categorical and Dirichlet distributions to capture and flexibly update beliefs about concepts, policies, and their relationships.
• Similarity metrics (e.g., cosine similarity between concepts' stimulus associations) for knowledge retrieval and expansion.
• Unsupervised, incremental structure growth: The agent automatically adds new concepts or policies when encountering unexplained data.

7. Distinctions from Prior Models and Limitations

Compared to semantic networks, this approach:

• Formally incorporates procedural and conditional knowledge with mathematically explicit mechanisms.
• Explains knowledge generation as both perceptual and action-driven (not just descriptive memory).
• Accommodates unsupervised model expansion and adaptation, accommodating novel experience continuously.

The model’s effectiveness hinges on accurate generative modeling and tractable inference over potentially high-dimensional spaces, as well as the definition of appropriate action and observation sets.

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Action knowledge, in this framework, is thus continuously generated, represented, and refined as the agent infers latent concept structure from perception and updates its procedural policies by acting to reduce prediction error, all driven by principled minimization of expected free energy and implemented via unsupervised, probabilistic computation (Ghasimi et al., 25 Jan 2025).