A Machine With Human-Like Memory
Project description
HumemAI Research
HumemAI Research explores human-like memory systems for AI — combining episodic (experience-based) and semantic (knowledge-based) memory models.
We study how machines can store, retrieve, and reason over structured memory graphs built from text, tables, and user interactions.
Installation
pip install humemai-research
Usage
from humemai_research.rdflib import Humemai
# or
from humemai_research.janusgraph import Humemai
Research Areas
- Episodic Memory: Representing conversations and experiences as temporal property graphs.
- Semantic Memory: Integrating user-provided or external data (e.g. Wikidata, Wikipedia) into graph, table, and vector formats.
- Memory Management: Learning what to remember, summarize, or forget across time.
- Graph-Based Reasoning: Querying and updating symbolic–neural hybrid memories.
- Reinforcement Learning & Knowledge Graphs: Using RL to induce hierarchies and explore knowledge structures.
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