Learning to Win by Reading Manuals in a Monte-Carlo Framework (1401.5390v1)

Abstract: Domain knowledge is crucial for effective performance in autonomous control systems. Typically, human effort is required to encode this knowledge into a control algorithm. In this paper, we present an approach to language grounding which automatically interprets text in the context of a complex control application, such as a game, and uses domain knowledge extracted from the text to improve control performance. Both text analysis and control strategies are learned jointly using only a feedback signal inherent to the application. To effectively leverage textual information, our method automatically extracts the text segment most relevant to the current game state, and labels it with a task-centric predicate structure. This labeled text is then used to bias an action selection policy for the game, guiding it towards promising regions of the action space. We encode our model for text analysis and game playing in a multi-layer neural network, representing linguistic decisions via latent variables in the hidden layers, and game action quality via the output layer. Operating within the Monte-Carlo Search framework, we estimate model parameters using feedback from simulated games. We apply our approach to the complex strategy game Civilization II using the official game manual as the text guide. Our results show that a linguistically-informed game-playing agent significantly outperforms its language-unaware counterpart, yielding a 34% absolute improvement and winning over 65% of games when playing against the built-in AI of Civilization.

Learning to Win by Reading Manuals in a Monte-Carlo Framework

The paper "Learning to Win by Reading Manuals in a Monte-Carlo Framework," authored by S.R.K. Branavan, David Silver, and Regina Barzilay, presents an innovative approach to embedding domain knowledge into autonomous control systems through the incorporation of linguistic information. This research specifically addresses the challenge of grounding textual knowledge in the context of strategy games, where players typically rely on manuals for strategic advice. The authors propose a method for automatically interpreting text to enhance control performance in complex games without the need for manual annotation.

The core contribution of this paper lies in a framework that integrates text analysis directly with control strategies, jointly learning both based on the feedback signals received from the application environment. This is achieved within a Monte-Carlo Search framework, wherein the algorithm evaluates different strategies by simulating gameplay. The method uses a multi-layer neural network to model and approximate the action-value function in a Markov Decision Process (MDP), which inherently considers both game state-action attributes and textual features extracted from the game manual.

A significant experimental application of this framework is demonstrated in the strategy game Civilization II. Utilizing the official game manual, the linguistically-informed Monte-Carlo Search agent significantly surpasses its non-text-informed counterpart, achieving a prominent 34% absolute improvement in game wins. This result emphasizes the efficacy of the text integration approach, with the linguistically-aware agent winning over 65% of games against the built-in AI—in stark contrast to a win rate of 26.1% by the best performing text-unaware baseline.

The paper tackles three main challenges: (1) mapping textual content to the game state-action space to retrieve relevant strategic advice, (2) annotation-free parameter estimation using feedback signals like game scores, and (3) effectively integrating text-derived information with existing control mechanisms within the Monte-Carlo framework. By handling these challenges, the method autoselects the most relevant text segment, labels it with an appropriate predicate structure, and uses this information to bias action selection toward strategic decisions recommended in the manual.

This research holds practical implications for AI systems needing to assimilate and act upon external domain knowledge represented in textual form, especially in scenarios where prior supervised data is unavailable or impractical to obtain. Theoretically, it expands the possibilities for machine learning systems to autonomously interpret and leverage human-readable documents for enhanced decision-making.

Future research could explore further applications of this framework in diverse domains beyond games, such as robotic control or autonomous vehicles, where textual guidelines and manuals often guide operational strategies. Investigating a broader spectrum of linguistic complexity and various textual genres could also enhance the adaptability and robustness of such AI systems.

Overall, this paper makes significant strides in grounded language learning, setting a precedent for subsequent advancements in integrating linguistic knowledge into control algorithms, potentially improving how AI systems understand and utilize textual instructions across complex domains.