Eva is a multimodal conversational framework for building planning-based systems that help users accomplish their domain goals through collaborative dialogue. We argue that planning-based systems can do this by inferring users’ intentions, adopting goals to achieve them, developing plans to achieve those goals, detecting whether obstacles are present, finding plans to overcome them, and planning their actions, including speech acts. In the Eva framework, conversational agents can maintain and reason with their own beliefs, goals and intentions, and explicitly reason about those of their users. Belief reasoning is accomplished with a modal Horn-clause meta-interpreter. The planning and reasoning subsystems obey the principles of persistent goals and intentions, including the formation and decomposition of intentions to perform complex actions, as well as the conditions under which they can be given up. In virtue of its planning process, Eva treats its speech acts just like its other actions – physical acts affect physical states, digital acts affect digital states, and speech acts affect mental and social states. This general framework enables systems to plan a variety of speech acts including requests, informs, questions, confirmations, recommendations, offers, acceptances, greetings, and emotive expressions. Each of these has a formally specified semantics which is used during the planning and reasoning processes. Because Eva-based agents can keep track of different users’ mental states, they can engage in multi-party dialogues. Importantly, the framework supports systems’ explanations of their actions because they have created plans standing behind each of them. As a reaction to the near-universal focus on using large language models for every application, a trend has emerged recently towards integrated neuro-symbolic architectures. The Eva framework is an example of such an architecture in the area of collaborative dialogue systems.

Advanced social cognitive skills enhance the effectiveness of human-robot interactions. Research shows that an important precursor to the development of these abilities in humans is Theory of Mind (ToM) - the ability to attribute mental states to oneself and to others. In this work, we endow robots with ToM abilities and propose a ToM-based approach to proactive robotic assistance by appealing to epistemic planning techniques. Our evaluation shows that robots implementing our approach and demonstrating ToM are measurably more helpful and perceived by humans as more socially intelligent compared to robots with a deficit in ToM.

Sequence classification is the task of predicting a class label given a sequence of observations. In many applications such as healthcare monitoring or intrusion detection, early classification is crucial to prompt intervention. In this work, we learn sequence classifiers that favour early classification from an evolving observation trace. While many state-of-the-art sequence classifiers are neural networks, and in particular LSTMs, our classifiers take the form of finite state automata and are learned via discrete optimization. Our automata-based classifiers are interpretable---supporting explanation, counterfactual reasoning, and human-in-the-loop modification---and have strong empirical performance. Experiments over a suite of goal recognition and behaviour classification datasets show our learned automata-based classifiers to have comparable test performance to LSTM-based classifiers, with the added advantage of being interpretable.

The objective of goal recognition is to infer a goal that accounts for the observed behavior of an actor. In this work, we introduce and formalize the notion of active goal recognition in which we endow the observer with agency to sense, reason, and act in the world with a view to enhancing and possibly expediting goal recognition, and/or to intervening in goal achievement. To this end, we present an algorithm for active goal recognition and a landmark-based approach to the elimination of hypothesized goals which leverages automated planning. Experiments demonstrate the merits of providing agency to the observer, and the effectiveness of our approach in potentially enhancing the observational power of the observer, as well as expediting and in some cases making possible the recognition of the actor's goal.

Every compassionate and functioning society requires its members to have a capacity to adopt others' perspectives. As Artificial Intelligence (AI) systems are given increasingly sensitive and impactful roles in society, it is important to enable AI to wield empathy as a tool to benefit those it interacts with. In this paper, we work towards this goal by bringing together a number of important concepts: empathy, AI planning, and plan recognition (i.e., the problem of inferring an actor's plan and goal given observations about its behavior). We formalize the notions of Empathetic Planning and Empathetic Plan Recognition which are informed by the beliefs and affective state of the actor, and propose AI planning-based computational approaches. We illustrate the benefits of our approach by conducting a study with human participants.

Building intelligent agents that can believably interact with humans is a difficult yet important task in a host of applications, including therapy, education, and entertainment. We submit that in order to enhance believability, the agent's affective state should be accurately modeled and should realistically influence the agent's behavior. We propose a computational model of affect which incorporates an empirically-based interplay between its various affective components - personality, motivation, emotion, and mood. Further, our model captures a number of salient mechanisms that are observable in humans and that influence the agent's behavior. We are therefore hopeful that our model will facilitate more engaging and meaningful human-agent interactions. We evaluate our model and illustrate its efficacy, as well as the importance of the different components in the model and their interplay.