Search Results for author:
Found 7 papers, 3 papers with code
Automatic Dataset Generation for Knowledge Intensive Question Answering Tasks
Mistral-7b-v0. 3 outperforms Llama-3-8b with BERT F1, BLEU, and ROUGE scores 0. 858, 0. 172, and 0. 260 of for the LLM generated QA pairs compared to scores of 0. 836, 0. 083, and 0. 139 for the human annotated QA pairs.
Speaking Your Language: Spatial Relationships in Interpretable Emergent Communication
The results indicate that agents can develop a language capable of expressing the relationships between parts of their observation, achieving over 90% accuracy when trained in a referential game which requires such communication.
Agent based modelling for continuously varying supply chains
Academic/practical: However, learning in continuously varying environments remains a challenge in the reinforcement learning literature. Methodology: This paper therefore seeks to address whether agents can control varying supply chain problems, transferring learning between environments that require different strategies and avoiding catastrophic forgetting of tasks that have not been seen in a while.
It's About Time: Temporal References in Emergent Communication
Emergent communication studies the development of language between autonomous agents, aiming to improve understanding of natural language evolution and increase communication efficiency.
The Effect of Epigenetic Blocking on Dynamic Multi-Objective Optimisation Problems
The mechanism shows improved performance on 12 of the 16 test problems, providing initial evidence that more algorithms should explore the wealth of epigenetic mechanisms seen in the natural world.
Epigenetic opportunities for Evolutionary Computation
The analysis shows that Darwinism and the Modern Synthesis have been incorporated into Evolutionary Computation but that the Extended Evolutionary Synthesis has been broadly ignored beyond:cultural inheritance, incorporated in the sub-set of Swarm Intelligence algorithms, evolvability, through CMA-ES, and multilevel selection, through Multi-Level Selection Genetic Algorithm.
Lifetime policy reuse and the importance of task capacity
A long-standing challenge in artificial intelligence is lifelong reinforcement learning, where learners are given many tasks in sequence and must transfer knowledge between tasks while avoiding catastrophic forgetting.
