An important feature of pervasive, intelligent assistance systems is the ability to dynamically adapt to the current needs of their users. Hence, it is critical for such systems to be able to recognize those goals and needs based on observations of the user's actions and state of the environment. In this work, we investigate the application of two state-of-the-art, planning-based plan recognition approaches in a real-world setting. So far, these approaches were only evaluated in artificial settings in combination with agents that act perfectly rational. We show that such approaches have difficulties when used to recognize the goals of human subjects, because human behaviour is typically not perfectly rational. To overcome this issue, we propose an extension to the existing approaches through a classification-based method trained on observed behaviour data. We empirically show that the proposed extension not only outperforms the purely planning-based- and purely data-driven goal recognition methods but is also able to recognize the correct goal more reliably, especially when only a small number of observations were seen. This substantially improves the usefulness of hybrid goal recognition approaches for intelligent assistance systems, as recognizing a goal early opens much more possibilities for supportive reactions of the system.

Time management under uncertainty is essential to large scale projects. From space exploration to industrial production, there is a need to schedule and perform activities. given complex specifications on timing. In order to generate schedules that are robust to uncertainty in the duration of activities, prior work has focused on a problem framing that uses an interval-bounded uncertainty representation. However, such approaches are unable to take advantage of known probability distributions over duration. In this paper we concentrate on a probabilistic formulation of temporal problems with uncertain duration, called the probabilistic simple temporal problem. As distributions often have an unbounded range of outcomes, we consider chance-constrained solutions, with guarantees on the probability of meeting temporal constraints. By considering distributions over uncertain duration, we are able to use risk as a resource, reason over the relative likelihood of outcomes, and derive higher utility solutions. We first demonstrate our approach by encoding the problem as a convex program. We then develop a more efficient hybrid algorithm whose parent solver generates risk allocations and whose child solver generates schedules for a particular risk allocation. The child is made efficient by leveraging existing interval-bounded scheduling algorithms, while the parent is made efficient by extracting conflicts over risk allocations. We perform numerical experiments to show the advantages of reasoning over probabilistic uncertainty, by comparing the utility of schedules generated with risk allocation against those generated from reasoning over bounded uncertainty. We also empirically show that solution time is greatly reduced by incorporating conflict-directed risk allocation.

Plan recognition, the problem of inferring the goals or plans of an observed agent, is a key element of situation awareness in human-machine and machine-machine interactions for many applications. Some plan recognition algorithms require knowledge about the potential behaviours of the observed agent in the form of a plan library, together with a decision model about how the observed agent uses the plan library to make decisions. It is however difficult to elicit and specify the decision model a priori. In this paper, we present a recursive neural network model that learns such a decision model automatically. We discuss promising experimental results of the approach with comparisons to selected state-of-the-art plan recognition algorithms on three benchmark domains.

Plan recognition is one of the fundamental problems of AI, applicable to many domains, from user interfaces to cyber security. We focus on a class of algorithms that use plan libraries as input to the recognition process. Despite the prevalence of these approaches, they lack a standard representation, and have not been compared to each other on common test bed. This paper directly addresses this gap by providing a standard plan library representation and evaluation criteria to consider. Our representation is comprehensive enough to describe a variety of known plan recognition problems, yet it can be easily applied to existing algorithms, which can be evaluated using our defined criteria. We demonstrate this technique on two known algorithms, SBR and PHATT. We provide meaningful insights both about the differences and abilities of the algorithms. We show that SBR is superior to PHATT both in terms of computation time and space, but at the expense of functionality and compact representation. We also show that depth is the single feature of a plan library that increases the complexity of the recognition, regardless of the algorithm used.

Interactive narratives suffer from the narrative paradox: the tension that exists between providing a coherent narrative experience and allowing a player free reign over what she can manipulate in the environment. Knowing what actions a player in such an environment intends to carry out would help in managing the narrative paradox, since it would allow us to anticipate potential threats to the intended narrative experience and potentially mediate or eliminate them. The process of observing player actions and attempting to come up with an explanation for those actions (i.e. the plan that the player is trying to carry out) is the problem of plan recognition. We adopt the framing of narratives as plans and leverage recent advances that cast plan recognition as planning to develop a symbolic plan recognition system as a proof-of-concept model of a player's reasoning in an interactive narrative environment. In this paper we outline the system architecture, report on performance metrics that demonstrate adequate performance for non-trivial domains, and discuss the implications of treating players as plan recognizers.

Opponent modeling is the ability to use prior knowledge and observations in order to predict the behavior of an opponent. This survey presents a comprehensive overview of existing opponent modeling techniques for adversarial domains, many of which must address stochastic, continuous, or concurrent actions, and sparse, partially observable payoff structures. We discuss all the components of opponent modeling systems, including feature extraction, learning algorithms, and strategy abstractions. These discussions lead us to propose a new form of analysis for describing and predicting the evolution of game states over time. We then introduce a new framework that facilitates method comparison, analyze a representative selection of techniques using the proposed framework, and highlight common trends among recently proposed methods. Finally, we list several open problems and discuss future research directions inspired by AI research on opponent modeling and related research in other disciplines.

Intention recognition is an important step to facilitate collaboration in multi-agent systems. Existing work mainly focuses on intention recognition in a single-agent setting and uses a descriptive model, e.g. Bayesian networks, in the recognition process. In this paper, we resort to a prescriptive approach to model agents' behaviour where which their intentions are hidden in implementing their plans. We introduce landmarks into the behavioural model therefore enhancing informative features for identifying common intentions for multiple agents. We further refine the model by focusing only action sequences in their plan and provide a light model for identifying and comparing their intentions. The new models provide a simple approach of grouping agents' common intentions upon partial plans observed in agents' interactions. We provide experimental results in support.

In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user’s expectation. We frame Explainable AI Planning as an iterative plan exploration process, in which the user asks a succession of contrastive questions that lead to the generation and solution of hypothetical planning problems that are restrictions of the original problem. The object of the exploration is for the user to understand the constraints that govern the original plan and, ultimately, to arrive at a satisfactory plan. We present the results of a user study that demonstrates that when users ask questions about plans, those questions are usually contrastive, i.e. “why A rather than B?”. We use the data from this study to construct a taxonomy of user questions that often arise during plan exploration. Our approach to iterative plan exploration is a process of successive model restriction. Each contrastive user question imposes a set of constraints on the planning problem, leading to the construction of a new hypothetical planning problem as a restriction of the original. Solving this restricted problem results in a plan that can be compared with the original plan, admitting a contrastive explanation. We formally define model-based compilations in PDDL2.1 for each type of constraint derived from a contrastive user question in the taxonomy, and empirically evaluate the compilations in terms of computational complexity. The compilations were implemented as part of an explanation framework supporting iterative model restriction. We demonstrate its benefits in a second user study.

Now, with mining jobs hard to find, he's cleaning up the mess the industry left behind. The 68-year-old operates a bucket loader scraping away red, rocky waste dumped years ago by failed coal mine operators in a valley in the town of Clinchco, Virginia. The $17.50 an hour before overtime he makes cleaning up massive "gob piles," as the locals call them, is less than what he earned in decades as a miner. "If this work goes away, I don't know what I would do," Mullins said. Appalachia, long the heart of the U.S. coal-mining industry, may be set for a surge in jobs like Mullins' if President Joe Biden is successful in his ambitions to transition the United States to a cleaner energy economy to fight climate change.

Sen. Marsha Blackburn, R-Tenn., on introducing legislation aimed at increasing national security. During his first press conference as president, Joe Biden claimed that border officials were "sending back the vast majority of the families" arriving at our southern border. Unfortunately, as every outlet that covered the issue has since reported, Biden's statement has proven false. Before he took the oath of office, then-candidate Biden was on the campaign trail promising amnesty and open borders. Now, Biden's refusal to enforce the law is allowing thousands of migrants to cross the border illegally every day, even as overworked Customs and Border Patrol agents collect, transport, and process thousands more.