Leveraging Knowledge Graph-Based Human-Like Memory Systems to Solve Partially Observable Markov Decision Processes
Humans observe only part of their environment at any moment but can still make complex, long-term decisions thanks to our long-term memory. To test how an AI can learn and utilize its long-term memory, we have developed a partially observable Markov decision processes (POMDP) environment, where the agent has to answer questions while navigating a maze. The environment is completely knowledge graph (KG) based, where the hidden states are dynamic KGs. A KG is both human- and machine-readable, making it easy to see what the agents remember and forget. We train and compare agents with different memory systems, to shed light on how human brains work when it comes to managing its own memory. By repurposing the given learning objective as learning a memory management policy, we were able to capture the most likely hidden state, which is not only interpretable but also reusable.
Code (1)
Tasks
RoomEnv-v2Similar Papers 제목 키워드 기반
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks
How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans. To handle this problem, memory networks are usually a great choice and a promisi…
DecoderWeakly-Supervised Opinion Summarization by Leveraging External Information
Opinion summarization from online product reviews is a challenging task, which involves identifying opinions related to various aspects of the product being reviewed. While previous works require additional human effort …
Opinion SummarizationA Machine with Short-Term, Episodic, and Semantic Memory Systems
Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. To evaluate…
Q-LearningReinforcement Learning (RL)RoomEnv-v1Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowl…
Decision MakingGraph Attentionreinforcement-learningReinforcement Learning (RL)+1Towards hypergraph cognitive networks as feature-rich models of knowledge
Semantic networks provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns. Pairwise connections …