Search Results for author:
Found 7 papers, 1 papers with code
Human-Centric Goal Reasoning with Ripple-Down Rules
ActorSim is a goal reasoning framework developed at the Naval Research Laboratory.
Interpretable ML for Imbalanced Data
We propose a set of techniques that can be used by both deep learning model users to identify, visualize and understand class prototypes, sub-concepts and outlier instances; and by imbalanced learning algorithm developers to detect features and class exemplars that are key to model performance.
Self-directed Learning of Action Models using Exploratory Planning
Complex, real-world domains may not be fully modeled for an agent, especially if the agent has never operated in the domain before.
Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial Intelligence
In this paper we provide (1) a description of the state space of Dungeon Crawl Stone Soup, (2) a description of the components for our API, and (3) the potential benefits of evaluating AI agents in the Dungeon Crawl Stone Soup video game.
Cost-Optimal Algorithms for Planning with Procedural Control Knowledge
There is an impressive body of work on developing heuristics and other reasoning algorithms to guide search in optimal and anytime planning algorithms for classical planning.
Keypoint Density-based Region Proposal for Fine-Grained Object Detection and Classification using Regions with Convolutional Neural Network Features
Although recent advances in regional Convolutional Neural Networks (CNNs) enable them to outperform conventional techniques on standard object detection and classification tasks, their response time is still slow for real-time performance.
Spontaneous Analogy by Piggybacking on a Perceptual System
Using this representation, our system leverages perceptual algorithms to automatically create an ontology of relational structures and to efficiently retrieve analogs for new relational structures from long-term memory.
