Eye gaze has the potential to provide insight into the minds of individuals, and this idea has been used in prior research to improve human goal recognition by combining human's actions and gaze. However, most existing research assumes that people are rational and honest. In adversarial scenarios, people may deliberately alter their actions and gaze, which presents a challenge to goal recognition systems. In this paper, we present new models for goal recognition under deception using a combination of gaze behaviour and observed movements of the agent. These models aim to detect when a person is deceiving by analysing their gaze patterns and use this information to adjust the goal recognition. We evaluated our models in two human-subject studies: (1) using data collected from 30 individuals playing a navigation game inspired by an existing deception study and (2) using data collected from 40 individuals playing a competitive game (Ticket To Ride). We found that one of our models (Modulated Deception Gaze Ontic) offers promising results compared to the previous state-of-the-art model in both studies. Our work complements existing adversarial goal recognition systems by equipping these systems with the ability to tackle ambiguous gaze behaviours.

A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates. Known as "ad hoc teamwork", enabling such a capability has been receiving increasing attention in the research community. One of the central challenges in ad hoc teamwork is quickly recognizing the current plans of other agents and planning accordingly. In this paper, we focus on the scenario in which teammates can communicate with one another, but only at a cost. Thus, they must carefully balance plan recognition based on observations vs. that based on communication. This paper proposes a new metric for evaluating how similar are two policies that a teammate may be following - the Expected Divergence Point (EDP). We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly. We demonstrate the effectiveness of this algorithm in a range of increasingly general communication in ad hoc teamwork problems.

Planning with epistemic goals has received attention from both the dynamic logic and planning communities. In the single-agent case, under the epistemic closed-world assumption (ECWA), epistemic planning can be reduced to contingent planning. However, it is inappropriate to make the ECWA in some epistemic planning scenarios, for example, when the agent is not fully introspective, or when the agent wants to devise a generic plan that applies to a wide range of situations. In this paper, we propose a complete single-agent epistemic planner without the ECWA. We identify two normal forms of epistemic formulas: weak minimal epistemic DNF and weak minimal epistemic CNF, and present the progression and entailment algorithms based on these normal forms. We adapt the PrAO algorithm for contingent planning from the literature as the main planning algorithm and develop a complete epistemic planner called EPK. Our experimental results show that EPK can generate solutions effectively for most of the epistemic planning problems we have considered including those without the ECWA.