Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

Online Maintenance Prioritization via Monte Carlo Tree Search and Case-based Reasoning

When maintenance resources in a manufacturing system are limited, a challenge arises in determining how to allocate these resources among multiple competing maintenance jobs. We formulate this problem as an online prioritization problem using a Markov decision process (MDP) to model the system behavior and Monte Carlo tree search (MCTS) to seek optimal maintenance actions in various states of the system. Further, we use Case-based Reasoning (CBR) to retain and reuse search experience gathered from MCTS to reduce the computational effort needed over time and to improve decision-making efficiency. We demonstrate that our proposed method results in increased system throughput when compared to existing methods of maintenance prioritization while also reducing the time needed to identify optimal maintenance actions as more experience is gathered. This is especially beneficial in manufacturing settings where maintenance decisions must be made quickly.

Spectra to Structure: Deep Reinforcement Learning for Molecular Inverse Problem

Spectroscopy is the study of how matter interacts with electromagnetic radiations of specific frequencies that has led to several monumental discoveries in science. The spectra of any particular molecule is highly information-rich, yet the inverse relation from the spectra to the molecular structure is still an unsolved problem. Nuclear Magnetic Resonance (NMR) spectroscopy is one such critical tool in the tool-set for scientists to characterise any chemical sample. In this work, a novel framework is proposed that attempts to solve this inverse problem by navigating the chemical space to find the correct structure that resulted in the target spectra. The proposed framework uses a combination of online Monte- Carlo-Tree-Search (MCTS) and a set of offline trained Graph Convolution Networks to build a molecule iteratively from scratch. Our method is able to predict the correct structure of the molecule ∼80% of the time in its top 3 guesses. We believe that the proposed framework is a significant step in solving the inverse design problem of NMR spectra to molecule.

Teaching People by Justifying Tree Search Decisions: An Empirical Study in Curling

In this research note we show that a simple justification system can be used to teach humans non-trivial strategies of the Olympic sport of curling. This is achieved by justifying the decisions of Kernel Regression UCT (KR-UCT), a tree search algorithm that derives curling strategies by playing the game with itself. Given an action returned by KR-UCT and the expected outcome of that action, we use a decision tree to produce a counterfactual justification of KR-UCT’s decision. The system samples other possible outcomes and selects for presentation the outcomes that are most similar to the expected outcome in terms of visual features and most different in terms of expected end-game value. A user study with 122 people shows that the participants who had access to the justifications produced by our system achieved much higher scores in a curling test than those who only observed the decision made by KR-UCT and those with access to the justifications of a baseline system. This is, to the best of our knowledge, the first work showing that a justification system is able to teach humans non-trivial strategies learned by an algorithm operating in self play.