A Computational Interface to Translate Strategic Intent from Unstructured Language in a Low-Data Setting (2023)

Abstract: Many real-world tasks involve a mixed-initiative setup, wherein humans and AI systems collaboratively perform a task. While significant work has been conducted towards enabling humans to specify, through language, exactly how an agent should complete a task (i.e., low-level specification), prior work lacks on interpreting the high-level strategic intent of the human commanders. Parsing strategic intent from language will allow autonomous systems to independently operate according to the user’s plan without frequent guidance or instruction. In this paper, we build a computational interface capable of translating unstructured language strategies into actionable intent in the form of goals and constraints. Leveraging a game environment, we collect a dataset of over 1000 examples, mapping language strategies to the corresponding goals and constraints, and show that our model, trained on this dataset, significantly outperforms human interpreters in inferring strategic intent (i.e., goals and constraints) from language (p < 0.05). Furthermore, we show that our model (125M parameters) significantly outperforms ChatGPT for this task (p < 0.05) in a low-data setting.

## introduction effective communication is essential for the proper functioning of organizational teams. "commander's intent" is a method for developing a theory of mind utilized in many domains such as the search and rescue, pandemic response, military, etc @xcite @xcite . commanders and leaders often utilize the formulation of "commander's intent" to convey the tasks that need to be accomplished and engender an understanding of the criteria for success to their subordinates @xcite ). commander's intent could similarly function as * these authors contributed to this paper while they were at georgia institute of technology. figure 1: our work aims to facilitate humans to specify their strategy to an ai system via language. using the board game risk as a simulated environment, we collect language descriptions of a strategy (top-left) corresponding to a player's troop deployments @xcite . the player's selections are shown by the white icons, and the grey and black icons denote the troops of the two opposing players. each strategy corresponds to a set of goals (bottom-right) and constraints (top-right) the green and orange text corresponds to the language relating to constraints and goals respectively. an effective scaffold to represent a human's strategic intent in a mixed-initiative interaction @xcite ). commander's intent provides a functionality for expert-specifiers to engender a degree of "shared-cognition," between an aicollaborator and a human-specifier, by aligning the actions of the ai system to the human-specifiers values or reward function. commander's intent is formally represented by a set of goals and constraints. goals (or preferences) are categorized as a desirable set of states or affairs that the agent intends to obtain @xcite and constraints refer to conditions that are imposed on solutions formulated by an agent @xcite . translating unstructured language-based strategy into this machine-readable specification is a non-trivial challenge. this translation could be conducted via a human interpreter, however, interpreters with the requisite expertise will not always be available. alternatively, humans could utilize a structured interface to specify their intent. however, interfaces can become overly complicated, and humans become demotivated to work with an ai system when they cannot easily navigate the interface @xcite . enabling humans to express their strategic intent in everyday language provides an effective solution to these issues. in this paper, we develop an approach to solve a task we call automatic strategy translation , wherein we learn to infer strategic intent, in the form of goals and constraints, from language. prior work has developed methods to utilize language to specify policies of an ai agent @xcite @xcite or specify reward functions or tasks which can be optimized for, via reinforcement learning (rl) or a planner @xcite @xcite . however, our work is the first to translate language into goals and constraints, which can be applied towards constrained optimization approaches for directing agent behavior independent of the original human specifier. unlike prior work, we focus on interpreting language description of complex gameplay strategies, rather than simple individual commands (e.g., "move from a to b; open the door"). first, we collect a dataset of over 1000 examples mapping language to goals and constraints, leveraging a game environment of risk. next, we fine-tuned a pretrained roberta model @xcite , equipped with model augmentations and customized loss functions such as order-agnostic cross entropy @xcite , to infer goals and constraints from language strategy specifications. finally, we employ a human evaluation to test our approach. recent work has shown that automated evaluation metrics for language models may provide a misleading measure of performance @xcite . therefore, we design a head-to-head evaluation, whereby, we can directly compare our model to the average human intepreter. in addition to humans, we prompted chatgpt to perform the same task on a held-out set of 30 examples. we computed the statistical difference between our model and these baselines, providing a concrete measure of the relative efficacy of our approach. our contributions are as follows: ## related work this section covers prior work on learning strategies from language, as well as methods and datasets to enable humans to specify ai-behavior in a mixed-initiative setting. ## natural language strategies in risk our work aims to facilitate humans to specify their strategy or commander's intent to an ai system via language. in this section, we utilize the board game risk to create a dataset that maps unstructured natural language descriptions of strategies to actionable intent in the form of goals and constraints. ## automatic strategy translation following the data collection in section 3, our goal is to leverage this dataset to develop a model that can perform the task of automatic strategy translation. inferring strategic intent from language is a non-trivial endeavor as unstructured language can be vague thus leading to ambiguous interpretations. we seek to develop an approach capable of performing this task better than the average human, so as to enable strategy specification via language to reduce the potential risk of human errors or the need of third-party expert interpreters. in this section, we cover the technical details which make this task possible in a low-data setting. ## experiments this section will present the empirical evaluations of our approach. we design two evaluation experiments to contrast our model's performance with humans, as well as chatgpt trained to perform our task through in-context learning. both human and chatgpt performance was computed using the 30 held-out examples in our test set. we statistically measure the difference in the average number of goals/constraints predicted correctly per data point between our model and the two baselines (human + chatgpt). we conclude with an ablation analysis across the model and data augmentations utilized in this approach. ## conclusion in this paper, we develop a novel computational interface to automate inferring strategic intent, in the form of goals and constraints, from unstructured language descriptions of strategies. we develop a new benchmark for our dataset and broader task, and further conduct a novel head-to-head evaluation to determine the relative efficacy of our approach. we show that in a low-data setting, our approach towards inferring goals and constraints from language strategy descriptions can significantly outperform humans for the same tasks. furthermore, we also found that our approach, with only 125 million parameters, was able to significantly outperform chatgpt for inferring strategic intent from language. our work endows researchers with valuable tools to further seldonian optimization approaches for mixed-initiative interaction.