72 citations · 77 across the 10 of their papers we have counts for
8 papers · 1 filter
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Neuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability
Taewoon Kim, Vincent François-Lavet, Michael Cochez
Partially observable reinforcement learning requires deciding what to retain, retrieve, and forget over time. We introduce a neuro-symbolic meta-policy that learns which symbolic m…
InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs
Mayank Kharbanda, Michael Cochez, Rajiv Ratn Shah +1
Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption. Current works mainly focus on Existential Fir…
Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Daniel Daza, Alberto Bernardi, Luca Costabello +4
Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such answers cannot be reached by di…
A Machine with Short-Term, Episodic, and Semantic Memory Systems
Taewoon Kim, Michael Cochez, Vincent François-Lavet +2
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 mod…
A Machine With Human-Like Memory Systems
Taewoon Kim, Michael Cochez, Vincent Francois-Lavet +2
Inspired by the cognitive science theory, we explicitly model an agent with both semantic and episodic memory systems, and show that it is better than having just one of the two me…