Author name cluster

Maayan Shvo

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6 papers

2 author rows

JAAMAS Journal 2025 Journal Article

A framework for building explainable collaborative multimodal dialogue systems using a theory of mind

Philip R. Cohen
Lucian Galescu
Maayan Shvo

Abstract 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.

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IROS Conference 2022 Conference Paper

Proactive Robotic Assistance via Theory of Mind

Maayan Shvo
Ruthrash Hari
Ziggy O'Reilly
Sophia Abolore
Sze-Yuh Nina Wang
Sheila A. McIlraith

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.

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ICAPS Conference 2022 Conference Paper

Resolving Misconceptions about the Plans of Agents via Theory of Mind

Maayan Shvo
Toryn Q. Klassen
Sheila A. McIlraith

For a plan to achieve some goal -- to be valid -- a set of sufficient and necessary conditions must hold. In dynamic settings, agents (including humans) may come to hold false beliefs about these conditions and, by extension, about the validity of their plans or the plans of other agents. Since different agents often believe different things about the world and about the beliefs of other agents, discrepancies may occur between agents' beliefs about the validity of plans. In this work, we explore how agents can use their Theory of Mind to resolve such discrepancies by communicating and/or acting in the environment. We appeal to an epistemic logic framework to allow agents to reason over other agents' nested beliefs, and demonstrate how epistemic planning tools can be used to resolve discrepancies regarding plan validity in a number of domains. Our work shows promise for human decision support as demonstrated by a user study that showcases the ability of our approach to resolve misconceptions held by humans.

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AAAI Conference 2021 Conference Paper

Interpretable Sequence Classification via Discrete Optimization

Maayan Shvo
Andrew C. Li
Rodrigo Toro Icarte
Sheila A. McIlraith

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 stateof-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.

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AAAI Conference 2020 Conference Paper

Active Goal Recognition

Maayan Shvo
Sheila A. McIlraith

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.

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AAMAS Conference 2019 Conference Paper

Towards Modeling the Interplay of Personality, Motivation, Emotion, and Mood in Social Agents

Maayan Shvo
Jakob Buhmann
Mubbasir Kapadia

Creating social agents that interact in believable ways is a challenging task. The agent’s emotional state must be faithfully modeled and should influence its behavior. In this paper, we introduce a computational model of affect which incorporates an empirically-based interplay between its various affective components - personality, motivation, emotion, and mood. These affective components as well as the relations between them capture a number of important mechanisms that are observable in human beings (e. g. , motivation driven planning, emotional reactions, or coping) and influence the agent’s decision making. Further, these mechanisms, reflected in the agent’s behavior, are integral to human-human interaction and are therefore likely to contribute to improved human-agent interaction. In a preliminary evaluation of our approach, we demonstrate the impact of the various components in the model and their interaction with one another on the agent’s decision making and behavior, by showing that the agent displays disparate behavior with and without the inclusion of specific components in our model.

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