Search Results for author:
Found 8 papers, 1 papers with code
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization
Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML.
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning
Search techniques, such as Monte Carlo Tree Search (MCTS) and Proof-Number Search (PNS), are effective in playing and solving games.
IRF: Interactive Recommendation through Dialogue
Recent research focuses beyond recommendation accuracy, towards human factors that influence the acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. We present a generic interactive recommender framework that can add interaction functionalities to non-interactive recommender systems. We take advantage of dialogue systems to interact with the user and we design a middleware layer to provide the interaction functions, such as providing explanations for the recommendations, managing users preferences learnt from dialogue, preference elicitation and refining recommendations based on learnt preferences.
An Evaluation Framework for Interactive Recommender System
Interactive recommender systems present an opportunity to engage the user in the process by allowing them to interact with the recommendations, provide feedback and impact the results in real-time.
Generating Dialogue Agents via Automated Planning
Dialogue systems have many applications such as customer support or question answering.
A Survey of Parallel A*
A* is a best-first search algorithm for finding optimal-cost paths in graphs.
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models
The paper presents and evaluates the power of parallel search for exact MAP inference in graphical models.
MAPP: a Scalable Multi-Agent Path Planning Algorithm with Tractability and Completeness Guarantees
However, such methods are incomplete and provide no guarantees with respect to the running time or the solution quality.
