Słownik AI
Kompletny słownik sztucznej inteligencji
Neural Logic Programming
Computational paradigm that combines the principles of logic programming with neural network architectures to enable logical reasoning in a deep learning framework. This approach facilitates the integration of structured symbolic knowledge into machine learning models.
Neural Probabilistic Reasoning
Method that combines probabilistic uncertainty with the reasoning capabilities of neural networks to perform inferences in complex and noisy environments. This approach allows modeling probability distributions over logical propositions.
Neuro-Symbolic Integration
Systematic process of merging sub-symbolic neural approaches with traditional symbolic methods to create more robust and interpretable hybrid AI systems. The integration aims to combine the best of both paradigms: neural learning and symbolic reasoning.
Deep Logical Inference
Inference mechanism that applies formal logical principles through multiple layers of deep neural representation to perform complex reasoning. Logical operations are hierarchically distributed within the network architecture.
Reasoning-based Learning
Learning paradigm where the model improves its performance by explicitly performing logical reasoning steps during the training process, making learning more transparent and controllable. This approach integrates logical constraints into the loss function.
Neural Knowledge Graphs
Hybrid data structures that represent knowledge in the form of graphs while using neural embeddings to encode entities and relations, enabling both symbolic and sub-symbolic inferences. These models facilitate reasoning over structured knowledge.
Symbolic Attention Mechanisms
Attention systems that operate on explicit symbolic representations rather than pure continuous embeddings, enabling more interpretable selection of relevant information. These mechanisms maintain the traceability of attention decisions.
Tensor Reasoning Networks
Neuro-symbolic architecture that uses tensor operations to model complex logical relationships and perform multi-hop reasoning on knowledge graphs. These networks represent facts and rules as high-dimensional tensors.
Neural Description Logic
Integration of description logic concepts into neural architectures to enable reasoning about ontologies and conceptual hierarchies with learning. This approach combines the expressive power of description logic with the flexibility of deep learning.
Neural Proof Systems
Mechanisms that use neural networks to guide or automate the process of logical proof search, combining learned heuristics with formal demonstration strategies. These systems can learn effective proof strategies from examples.
Statistical Logic Programming
Extension of logic programming that integrates probabilistic models to handle uncertainty in facts and rules, often implemented with neural architectures for learning. This approach allows reasoning about uncertain worlds with structured knowledge.
Hybrid Neuro-Symbolic Architecture
Computational structure that explicitly combines neural and symbolic components in a unified architecture, enabling bidirectional interactions between subsymbolic perception and symbolic reasoning. These architectures aim to overcome the limitations of each approach taken in isolation.
Neural Inductive Reasoning
Process that uses neural networks to generalize from specific examples to more general rules or principles, mimicking human inductive reasoning with machine learning capabilities. This approach enables automatic discovery of logical regularities.
Neural Logical Constraints
Mechanism that incorporates formal logical constraints directly into a neural network's architecture or loss function, guiding learning toward solutions consistent with prior knowledge. These constraints ensure the logical validity of predictions.
Neural Explanation Systems
Neuro-symbolic mechanisms that generate interpretable symbolic explanations for neural network decisions by linking activations to logical concepts. These systems translate neural reasoning into understandable logical chains.
Deep Relational Learning
Paradigm that extends deep learning to explicitly process structured relational data using neural architectures specialized in reasoning about relationships. This approach combines the power of deep networks with modeling of structural relations.
Neural Compositionality Mechanisms
Neural architectures explicitly designed to respect compositional principles, where the meaning of complex expressions is constructed from those of their components. These mechanisms enable structured and generalizable reasoning.