The problem of goal recognition, and its more general form, plan recognition, have been the subjects of extensive investigation in the AI community. However, relatively little effort has been undertaken to examine goal recognition in interactive narrative. In this paper, we propose a research agenda to improve the accuracy of goal recognition models for interactive narratives using explicit representations of narrative structure inspired by the natural language processing community. We describe a particular category of narrative representations, narrative schemas, that we anticipate will effectively capture patterns of player behavior in interactive narratives and improve the accuracy of goal recognition models.

The problem of goal recognition, and its more general form plan recognition, has been the subject of extensive investigation in the AI community. However, there have been relatively few empirical investigations of goal recognition models in the intelligent narrative technologies community to date, and little is known about how computational models of interactive narrative can inform goal recognition. In this paper, we investigate a novel goal recognition model based on Markov Logic Networks (MLNs) that leverages narrative discovery events to enrich its representation of narrative state. An empirical evaluation shows that the enriched model outperforms a prior state-of-the-art MLN model in terms of accuracy, convergence rate, and the point of convergence.

Recent years have seen a growing interest in player modeling, which supports the creation of player-adaptive digital games. A central problem of player modeling is goal recognition, which aims to recognize players' intentions from observable gameplay behaviors. Player goal recognition offers the promise of enabling games to dynamically adjust challenge levels, perform procedural content generation, and create believable NPC interactions. A growing body of work is investigating a wide range of machine learning-based goal recognition models. In this paper, we introduce GOALIE, a multidimensional framework for evaluating player goal recognition models.

Recent years have seen a growing interest in player modeling to create player-adaptive digital games. As a core player-modeling task, goal recognition aims to recognize players' latent, high-level intentions in a non-invasive fashion to deliver goal-driven, tailored game experiences. This paper reports on an investigation of multimodal data streams that provide rich evidence about players' goals. Two data streams, game event traces and player gaze traces, are utilized to devise goal recognition models from a corpus collected from an open-world serious game for science education. Empirical evaluations of 140 players' trace data suggest that multimodal LSTM-based goal recognition models outperform competitive baselines, including unimodal LSTMs as well as multimodal and unimodal CRFs, with respect to predictive accuracy and early prediction. The results demonstrate that player gaze traces have the potential to significantly enhance goal recognition models' performance.

Goal recognition design (GRD) facilitates understanding the goals of acting agents through the analysis and redesign of goal recognition models, thus offering a solution for assessing and minimizing the maximal progress of any agent in the model before goal recognition is guaranteed. In a nutshell, given a model of a domain and a set of possible goals, a solution to a GRD problem determines (1) the extent to which actions performed by an agent within the model reveal the agent’s objective; and (2) how best to modify the model so that the objective of an agent can be detected as early as possible. This approach is relevant to any domain in which rapid goal recognition is essential and the model design can be controlled. Applications include intrusion detection, assisted cognition, computer games, and human-robot collaboration. A GRD problem has two components: the analyzed goal recognition setting, and a design model specifying the possible ways the environment in which agents act can be modified so as to facilitate recognition. This work formulates a general framework for GRD in deterministic and partially observable environments, and offers a toolbox of solutions for evaluating and optimizing model quality for various settings. For the purpose of evaluation we suggest the worst case distinctiveness (WCD) measure, which represents the maximal cost of a path an agent may follow before its goal can be inferred by a goal recognition system. We offer novel compilations to classical planning for calculating WCD in settings where agents are bounded-suboptimal. We then suggest methods for minimizing WCD by searching for an optimal redesign strategy within the space of possible modifications, and using pruning to increase efficiency. We support our approach with an empirical evaluation that measures WCD in a variety of GRD settings and tests the efficiency of our compilation-based methods for computing it. We also examine the effectiveness of reducing WCD via redesign and the performance gain brought about by our proposed pruning strategy.

Player goal recognition in digital games offers the promise of enabling games to dynamically customize player experience. Goal recognition aims to recognize players’ high-level intentions using a computational model trained on a player behavior corpus. A significant challenge is posed by devising reliable goal recognition models with a behavior corpus characterized by highly idiosyncratic player actions. In this paper, we introduce deep LSTM-based goal recognition models that handle the inherent uncertainty stemming from noisy, non-optimal player behaviors. Empirical evaluation indicates that deep LSTMs outperform competitive baselines including single-layer LSTMs, n-gram encoded feedforward neural networks, and Markov logic networks for a goal recognition corpus collected from an open-world educational game. In addition to metric-based goal recognition model evaluation, we investigate a visualization technique to show a dynamic goal recognition model’s performance over the course of a player’s goal-seeking behavior. Deep LSTMs, which are capable of both sequentially and hierarchically extracting salient features of player behaviors, show significant promise as a goal recognition approach for open-world digital games.

Computational models for goal recognition hold great promise for enhancing the capabilities of drama managers and director agents for interactive narratives. The problem of goal recognition, and its more general form, plan recognition, have been the subjects of extensive investigation in the AI community. However, relatively little effort has been undertaken to examine goal recognition in interactive narrative. In this paper, we propose a research agenda to improve the accuracy of goal recognition models for interactive narratives using explicit representations of narrative structure inspired by the natural language processing community. We describe a particular category of narrative representations, narrative schemas, that we anticipate will effectively capture patterns of player behavior in interactive narratives and improve the accuracy of goal recognition models.

Goal recognition in digital games involves inferring players’ goals from observed sequences of low-level player actions. Goal recognition models support player-adaptive digital games, which dynamically augment game events in response to player choices for a range of applications, including entertainment, training, and education. However, digital games pose significant challenges for goal recognition, such as exploratory actions and ill-defined goals. This paper presents a goal recognition framework based on Markov logic networks (MLNs). The model’s parameters are directly learned from a corpus that was collected from player interactions with a non-linear educational game. An empirical evaluation demonstrates that the MLN goal recognition framework accurately predicts players’ goals in a game environment with exploratory actions and ill-defined goals.

Goal recognition is the task of inferring users’ goals from sequences of observed actions. By enabling player-adaptive digital games to dynamically adjust their behavior in concert with players’ changing goals, goal recognition can inform adaptive decision making for a broad range of entertainment, training, and education applications. This paper presents a goal recognition framework based on Markov logic networks (MLN). The model’s parameters are directly learned from a corpus of actions that was collected through player interactions with a non-linear educational game. An empirical evaluation demonstrates that the MLN goal recognition framework accurately predicts players’ goals in a game environment with multiple solution paths.