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.

This paper documents extending the ELEXIR (Engine for LEXicalized Intent Recognition) system (Geib 2009; Geib 2011) with a world model. This is a significant increase in the expressiveness of the plan recognition system and allows a number of additions to the algorithm, most significantly conditioning probabilities for recognized plans on the state of the world during execution. Since, ELEXIR falls in the family of gramatical methods for plan recognition in viewing the problem of plan recognition as that of parsing, this paper will also briefly discuss how this extension relates to state of the art proposals in the natural language community regarding probabilistic parsing.

Much of the existing work on plan recognition assumes that actions of other agents can be observed directly. In continuous temporal domains such as traffic scenarios this assumption is typically not warranted. Instead, one is only able to observe facts about the world such as vehicle positions at different points in time, from which the agents' intentions need to be inferred. In this paper we show how this problem can be addressed in the situation calculus and a new variant of the action programming language Golog, which includes features such as continuous time and change, stochastic actions, nondeterminism, and concurrency. In our approach we match observations against a set of candidate plans in the form of Golog programs. We turn the observations into actions which are then executed concurrently with the given programs. Using decision-theoretic optimization techniques those programs are preferred which bring about the observations at the appropriate times. Besides defining this new variant of Golog we also discuss an implementation and experimental results using driving maneuvers as an example.

The task of keyhole (unobtrusive) plan recognition is central to adaptive game AI. "Tech trees" or "build trees" are the core of real-time strategy (RTS) game strategic (long term) planning. This paper presents a generic and simple Bayesian model for RTS build tree prediction from noisy observations, which parameters are learned from replays (game logs). This unsupervised machine learning approach involves minimal work for the game developers as it leverage players' data (com- mon in RTS). We applied it to StarCraft1 and showed that it yields high quality and robust predictions, that can feed an adaptive AI.

The task of keyhole (unobtrusive) plan recognition is central to adaptive game AI. “Tech trees” or “build trees” are the core of real-time strategy (RTS) game strategic (long term) planning. This paper presents a generic and simple Bayesian model for RTS build tree prediction from noisy observations, which parameters are learned from replays (game logs). This unsupervised machine learning approach involves minimal work for the game developers as it leverage players’ data (com- mon in RTS). We applied it to StarCraft1 and showed that it yields high quality and robust predictions, that can feed an adaptive AI.

Abduction is widely used in the task of plan recognition, since it can be viewed as the task of finding the best explanation for a set of observations. The major drawback of abduction is its computational complexity. The task of abductive reasoning quickly becomes intractable as the background knowledge is increased. Recent efforts in the field of computational linguistics have enriched computational resources for commonsense reasoning. The enriched knowledge base facilitates exploring practical plan recognition models in an open-domain. Therefore, it is essential to develop an efficient framework for such large-scale processing. In this paper, we propose an efficient implementation of Weighted abduction. Our framework transforms the problem of explanation finding in Weighted abduction into a linear programming problem. Our experiments showed that our approach efficiently solved problems of plan recognition and outperforms state-of-the-art tool for Weighted abduction.

Previous work has suggested the use of lexicalized grammars for probabilistic plan recognition. Such grammars allow the domain builder to delay commitment to hypothesizing high level goals in order to reduce computational costs. However this delay has limitations. In the case of only partial observation traces, delaying commitment can prevent such algorithms from forming correct conclusions about some goals. This paper presents a heuristic metric to address this limitation. It advocates computing the maximum change in conditional probability across all the computed explanations given the observations explicitly considering a goal of interest.

The problem of identifying the (dynamic) team structures and team behaviors from the observed activities of multiple agents is called Multi-Agent Plan Recognition (MAPR). We extend a recent formalization of this problem to accommodate a compact, partially ordered, multi-agent plan language, as well as complex plan execution models — particularly plan abandonment and activity interleaving. We adopt a branch and price approach to solve MAPR in such a challenging setting, and fully instantiate the (generic) pricing problem for MAPR. We show experimentally that this approach outperforms a recently proposed hypothesis pruning algorithm in two domains: multi-agent blocks word, and intrusion detection. The key benefit of the branch and price approach is its ability to grow the necessary component (occurrence) space from which the hypotheses are constructed, rather than begin with a fully enumerated component space that has an intractable size, and search it with pruning. Our formulation of MAPR has the broad objective of bringing mature Operations Research methodologies to bear upon MAPR, envisaged to have a similar impact as mature SAT-solvers had on planning.

This work focuses on developing domain-independent heuristics for probabilistic planning problems characterized by full observability and non-deterministic effects of actions that are expressed by probability distributions. The approach is to first search for a high probability deterministic plan using a classical planner. A novel probabilistic plan graph heuristic is used to guide the search towards high probability plans. The resulting plans can be used in a system that handles unexpected outcomes by runtime replanning. The plans can also be incrementally augmented with contingency branches for the most critical action outcomes. This abstract will describe the steps that we have taken in completing the above work and the obtained results.

Agents in highly dynamic adversarial domains, such as RTS games, must continually make time-critical decisions to adapt their behaviour to the changing environment. In such a context, the planning agent must consider his opponent's actions as uncontrollable, or at best influenceable. In general nondeterministic domains where there is no clear turn-taking protocol, most heuristic search methods to date do not explicitly reason about the opponent's actions when guiding the state space exploration towards goal or high-reward states. In contrast, we are investigating a domain-independent heuristic planning approach which reasons about the dynamics and uncontrollability of the opponent's behaviours in order to provide better guidance to the search process of the planner. Our planner takes as input the opponent's behaviours recognized by a plan recognition module and uses them to identify opponent's actions that lead to low-utility projected states. We believe such explicit heuristic reasoning about the potential behaviours of the opponent is crucial when planning in adversarial domains, yet is missing in today's planning approaches.