| name | aif-message-passing |
| description | Converts a standard Bethe BP implementation to the Active Inference (AIF-MP) message-passing scheme. Specifies four channel reparameterizations, modified factor kernels, and correct fixed-point equations. Reference paper Appendix D (T=1 derivation) and D.6 (generic T). |
Active Inference Message Passing (AIF-MP)
AIF-MP is standard Bethe BP with entropy corrections reparameterized into modified factor kernels via four channel variables per time step. It combines the cross-entropy planning correction (VBP) with three epistemic corrections.
Reference: paper Section 5, Appendix D (full derivation for T=1), Section D.6 (generic T).
1. Objective
The combined EFE-based planning objective adds to the Bethe free energy (paper eq 13, Corollary 9 eq B.5):
$$\Delta F_{\mathrm{comb}} = \underbrace{\sum_t 2\mathbb{H}[q(y_t|x_t,\theta)] - \mathbb{H}[q(x_t|x_{t-1},u_t)] - \mathbb{H}[q(y_t|x_t)]}{\Delta^{\mathrm{AIF}}} + \underbrace{\sum_t \mathbb{H}[q(u_t|x{t-1})]}_{\Delta^{\mathrm{planning}}}$$
2. Four Channels per Time Step
Each time step $t$ has four channel variables (paper eq D.37):
| Channel | Symbol | Normalizes over | Enters kernel |
|---|
| Observation | $r_{y\mid x\theta,t}(y_t\mid x_t,\theta)$ | $\sum_{y_t} r = 1;\forall(x_t,\theta)$ | Obs numerator (squared) |
| Marginal observation | $r_{y\mid x,t}(y_t\mid x_t)$ | $\sum_{y_t} r = 1;\forall x_t$ | Obs denominator |
| Predictive dynamics | $r_{x\mid xu,t}(x_t\mid x_{t-1},u_t)$ | $\sum_{x_t} r = 1;\forall(x_{t-1},u_t)$ | Dyn denominator |
| Policy | $r_{u\mid x,t}(u_t\mid x_{t-1})$ | $\sum_{u_t} r = 1;\forall x_{t-1}$ | Dyn numerator |
Setting all four channels to uniform recovers standard Bethe BP.
3. Modified Kernels (paper eq D.38)
| Factor | AIF kernel |
|---|
| $f_{\mathrm{obs}_t}$ | $\displaystyle\frac{p(y_t\mid x_t,\theta)\cdot r_{y\mid x\theta,t}^2(y_t\mid x_t,\theta)}{r_{y\mid x,t}(y_t\mid x_t)}$ |
| $f_{\mathrm{dyn}_t}$ | $\displaystyle\frac{p(x_t\mid x_{t-1},\theta,u_t)\cdot r_{u\mid x,t}(u_t\mid x_{t-1})}{r_{x\mid xu,t}(x_t\mid x_{t-1},u_t)}$ |
The dynamics kernel combines the VBP action channel (numerator, sharpens policy) with the predictive dynamics channel (denominator, spreads mass over futures). See paper Remark 16.
4. Channel Updates (paper eq D.42)
At each iteration, channels recover conditionals from factor beliefs:
$$r_{y|x\theta,t}^* = q_t(y_t|x_t,\theta), \quad r_{y|x,t}^* = q_t(y_t|x_t), \quad r_{x|xu,t}^* = q_t(x_t|x_{t-1},u_t), \quad r_{u|x,t}^* = q_t(u_t|x_{t-1})$$
Computed from region beliefs:
- $r_{y|x\theta} \leftarrow q_{\mathrm{obs}} / q_{\mathrm{sep}}$ where $q_{\mathrm{sep}} = \sum_y q_{\mathrm{obs}}$
- $r_{y|x} \leftarrow q_{yx} / q_x$ where $q_{yx} = \sum_\theta q_{\mathrm{obs}}$
- $r_{x|xu} \leftarrow q_{\mathrm{trip}} / q_{\mathrm{pair}}$ where $q_{\mathrm{trip}} = \sum_\theta q_{\mathrm{dyn}}$, $q_{\mathrm{pair}} = \sum_x q_{\mathrm{trip}}$
- $r_{u|x} \leftarrow q_{\mathrm{pair}} / q_{x_{t-1}}$
5. Implementation Details (inference/active_inference.py)
Key differences from the generic paper scheme:
- theta handling: Uses $p(\theta)$ prior directly -- no theta cavity messages, no theta inference. This simplifies the iteration (no dyn->theta or obs->theta messages).
- Damping: Geometric damping in log-space:
damped = (1-alpha)*log_old + alpha*log_new, followed by renormalization. NOT arithmetic damping.
- Precomputed obs path: When
log_local_to_x is provided, obs channels are precomputed outside the planning loop via precompute_obs_channels(), since obs channel updates depend only on B, prior_theta, and damping -- not on fwd/bwd messages.
- theta-marginalized base: Precomputes
log_base = logsumexp(log_T_kernel + log_prior_theta, axis=theta) giving shape (S, S, A). The per-iteration dynamics kernel is then log_base - log_dyn_channels + log_r_ux (4D, no theta dimension).
- Return:
(action_dist, log_dyn_channels, log_obs_channels). log_obs_channels is None when using the precomputed obs path.
Iteration structure (precomputed obs path)
Initialize: r_{u|x} = uniform, r_{x|xu} = uniform
Precompute: log_base = marginalize_theta(T), log_local_to_x = obs_channels(B) + pref(C)
For each iteration:
1. Build 4D kernel: base / r_{x|xu} * r_{u|x}
2. Forward pass (inject log_local_to_x at each step)
3. Backward pass + action marginals
4. Compute theta-marginalized dyn region: kernel * fwd * bwd * action_prior
5. Dyn channels: normalize region over x_new
6. Action channels: marginalize x_new from region, normalize over u
7. Damp both channels (geometric)
Iteration structure (dense path, with obs channels in carry)
Same as above but additionally:
- Obs kernels recomputed each iteration:
B * r_{y|xθ} * r_{y|xθ}/r_{y|x}
- Obs->x messages recomputed from obs kernels
- Obs channels updated: normalize obs kernels over y
- Marginal obs channels: marginalize theta from obs kernels, normalize over y
6. Verification Checklist
