Search Results for author: Adi Botea

Found 8 papers, 1 papers with code

Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning

no code implementations NeurIPS 2019 Akihiro Kishimoto, Beat Buesser, Bei Chen, Adi Botea

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

no code implementations3 Oct 2019 Oznur Alkan, Massimiliano Mattetti, Elizabeth M. Daly, Adi Botea, Inge Vejsbjerg

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

no code implementations16 Apr 2019 Oznur Alkan, Elizabeth M. Daly, Adi Botea

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.

Recommendation Systems

A Survey of Parallel A*

1 code implementation16 Aug 2017 Alex Fukunaga, Adi Botea, Yuu Jinnai, Akihiro Kishimoto

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

no code implementations NeurIPS 2015 Akihiro Kishimoto, Radu Marinescu, Adi Botea

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

no code implementations16 Jan 2014 Ko-Hsin Cindy Wang, Adi Botea

However, such methods are incomplete and provide no guarantees with respect to the running time or the solution quality.

Problem Decomposition

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