The field of Explainable Artificial Intelligence has gained popularity in recent years, due to the need for users to understand AI-made decisions, in order to increase their trust in the AI system. However, not much work has been performed on explaining recommendations made by search algorithms, which do not focus on single decisions, but on complex plans of action. This paper investigates promising directions for research in Explainable Search (XS), by evaluating with a user study different types of explanations for a search-based algorithm. Preliminary results suggest that users prefer explanations generated using context-based features, which are not only based on the current state of the problem, but are extracted from different parts of the tree generated by the search algorithm.

Monte Carlo Tree Search (MCTS) is a heuristic search algorithm that can play a wide range of games without requiring any domain-specific knowledge. However, MCTS tends to struggle in very complicated games due to an exponentially increasing branching factor. A promising solution for this problem is to focus the search only on a small fraction of states. Subgoal Monte Carlo Tree Search (S-MCTS) achieves this by using a predefined subgoal-predicate that detects promising states called subgoals. However, not only does this make S-MCTS domain-dependent, but also it is often difficult to define a good predicate. In this paper, we propose using quality diversity (QD) algorithms to detect subgoals in real-time. Furthermore, we show how integrating QD-algorithms into S-MCTS significantly improves its performance in the Physical Travelling Salesmen Problem without requiring any domain-specific knowledge.

The research field of Artificial General Intelligence (AGI) is concerned with the creation of adaptive programs that can autonomously address tasks of a different nature. Search and planning have been identified as core capabilities of AGI, and have been successful in many scenarios that require sequential decision-making. However, many search algorithms are developed for specific problems and exploit domain-specific knowledge, which makes them not applicable to perform different tasks autonomously. Although some domain-independent search algorithms have been proposed, a programmer still has to make decisions on their design, setup and enhancements. Thus, the performance is limited by the programmer's decisions, which are usually biased. This paper proposes to develop a framework that, in line with the goals of AGI, autonomously addresses a wide variety of search tasks, adapting automatically to each new, unknown task. To achieve this, we propose to encode search algorithms in a formal language and combine algorithm portfolios with automatic algorithm generation. In addition, we see games as the ideal test bed for the framework, because they can model a wide variety of complex problems. Finally, we believe that this research will have an impact not only on the AG I research field, but also on the game industry and on real-world problems.

For general video game playing agents, the biggest challenge is adapting to the wide variety of situations they encounter and responding appropriately. Some success was recently achieved by modifying search-control parameters in agents on-line, during one play-through of a game. We propose adapting such methods for Rolling Horizon Evolutionary Algorithms, which have shown high performance in many different environments, and test the effect of on-line adaptation on the agent's win rate. On-line tuned agents are able to achieve results comparable to the state of the art, including first win rates in hard problems, while employing a more general and highly adaptive approach. We additionally include further insight into the algorithm itself, given by statistics gathered during the tuning process and highlight key parameter choices.

Accepted at ECAI 2020

Many enhancements for Monte Carlo tree search (MCTS) have been applied successfully in general game playing (GGP). MCTS and its enhancements are controlled by multiple parameters that require extensive and time-consuming offline optimization. Moreover, as the played games are unknown in advance, offline optimization cannot tune parameters specifically for single games. This paper proposes a self-adaptive MCTS strategy (SA-MCTS) that integrates within the search a method to automatically tune search-control parameters online per game. It presents five different allocation strategies that decide how to allocate available samples to evaluate parameter values. Experiments with 1 s play-clock on multiplayer games show that for all the allocation strategies the performance of SA-MCTS that tunes two parameters is at least equal to or better than the performance of MCTS tuned offline and not optimized per-game. The allocation strategy that performs the best is N-Tuple Bandit Evolutionary Algorithm (NTBEA). This strategy also achieves a good performance when tuning four parameters. SA-MCTS can be considered as a successful strategy for domains that require parameter tuning for every single problem, and it is also a valid alternative for domains where offline parameter tuning is costly or infeasible.

Monte-Carlo Tree Search (MCTS) has been applied successfully in many domains. Previous research has shown that adding randomization to certain components of MCTS might increase the diversification of the search and improve the performance. In a domain that tackles many games with different characteristics, like General Game Playing (GGP), trying to diversify the search might be a good strategy. This paper investigates the effect of randomizing search-control parameters for MCTS in GGP. Four different randomization strategies are compared and results show that randomizing parameter values before each simulation has a positive effect on the search in some of the tested games. Moreover, parameter randomization is compared with on-line parameter tuning.