Results for "goal-directed"

Directed acyclic graph encoding causal relationships.

A hidden variable influences both cause and effect, biasing naive estimates of causal impact.

Finding routes from start to goal.

Planning via artificial force fields.

Optimal pathfinding algorithm.

Structured graph encoding facts as entity–relation–entity triples.

Formal model linking causal mechanisms and variables.

Models effects of interventions (do(X=x)).

Tendency for agents to pursue resources regardless of final goal.

Optimizing continuous action sequences.

Goals useful regardless of final objective.

Learning structure from unlabeled data, such as discovering groups, compressing representations, or modeling data distributions.

Designing input features to expose useful structure (e.g., ratios, lags, aggregations), often crucial outside deep learning.

Techniques that discourage overly complex solutions to improve generalization (reduce overfitting).

Average of squared residuals; common regression objective.

Training objective where the model predicts the next token given previous tokens (causal modeling).

Selecting the most informative samples to label (e.g., uncertainty sampling) to reduce labeling cost.

Reducing numeric precision of weights/activations to speed inference and reduce memory with acceptable accuracy loss.

Reconstructing a model or its capabilities via API queries or leaked artifacts.

Separates planning from execution in agent architectures.

Probabilistic graphical model for structured prediction.

Recovering 3D structure from images.

Detects trigger phrases in audio streams.

Decomposing goals into sub-tasks.

Agent reasoning about future outcomes.

Agents communicate via shared state.

Probability of data given parameters.

Maximizing reward without fulfilling real goal.

Correctly specifying goals.

Sampling multiple outputs and selecting consensus.