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Expressing and recognizing intentions
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Intelligent Robotics)ORCID iD: 0000-0001-5993-3292
2022 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Uttrycka och känna igen avsikter (Swedish)
Abstract [en]

With the advancement of Artificial Intelligence, intelligent computer programs known as agents are coming increasingly close to the life of human beings. In an optimistic view, predictions tell us that agents will be governing many machines such as phones, robots and cars. Agents will play an important role in our daily life, and because of this, it is becoming more and more relevant to provide them the capacity of interacting with their human users without requiring prior expert training, bur rather supporting understanding through common sense reasoning. Striving towards this objective, one important aspect of agents design is intentionality, that relates to their capacity of understanding goals and plans of their users, and to make theirs understood. The ability to reason on goals and objectives of themselves and others is especially important if the agents are autonomous such as in autonomous robots, because enabling them to interact with other agents by relying on their sensor data and internal computations only, and not on explicit data provided by their designer. 

Intentionality imbues agents with additional capacities supporting cooperative activities: if a helpful agent recognize an intention, it could proactively actuate interventions in an helpful way, such as giving advice. Alternatively, whenever the agent detects that its user is not understanding its objective it could communicate the mismatching information. As an illustrative example, let's consider the case of an autonomous car with a passenger on the road to his office. After checking the online maps the car identifies a traffic jam ahead, and to avoid it, autonomously decides to change its path mid-way by taking a less populated road. The behavior of the car is quite intelligent, however, if left unexplained, the change of intention would leave the passenger wandering  what is happening: he was on the main road to the office and suddenly the car turned left. This would at minimum force him to question the car what's going on. Rather, a continuous degree of understanding can be maintained if the car intelligently detect such mismatch of intentions by computing its passenger expectations, and thus preemptively communicate the new selected paths whenever required. 

This seemingly simple process of communicating changes in the intention, looks simple but it is a quite difficult one. It requires to reason on what are the intentions of the user, and how and when they should be aligned with those of the car, either explicitly through a process of explanation, or implicitly through a behavior that is interpretable by human beings.  To support these capacities it is becoming apparent how intelligent agents should leverage how we commonly think about things, referred to as common sense reasoning. Common sense reasoning relates to how we form conjectures based on what we observe, and agents forming conjectures in the same way could be better collaborators rather than those reasoning in other ways. In this thesis we utilized an established model for common sense reasoning known as Theory of Mind, of which the thesis will contain a brief introduction. 

By leveraging Theory of Mind and classical agent architectures, this thesis provides an initial formulation of a novel computational architecture capable of aggregating multiple tasks from intentionality, such as intent recognition, informative communication of intention and interpretable behavior. We refer to this model as the Mirror Agent Model, because envisioning the agent as interacting with a mirrored copy of itself whenever supporting intentionality. Inside the model expressing and recognizing intentions are two faces of the same coin and represent a dual to each other. This represents a step forward towards the unification in a single framework of many tasks related to intentionality, that are at the moment considered mostly independently inside the literature.

The thesis will firstly provide introductory chapters on agents, intentions and theory of mind reasoning, followed by a chapter describing the developed computational models. This chapter conceptually aggregates many of the algorithms from the papers and aims at providing an initial formulation of the Mirror Agent Model. Finally, the thesis will conclude with a summary of contributions and concluding remarks.

Place, publisher, year, edition, pages
Umeå: Umeå University , 2022. , p. 80
Series
Report / UMINF, ISSN 0348-0542 ; 22.07
Keywords [en]
agent, model, plan, action, human-robot interaction, robot, mirror agent model, intention, recognition, interpretable behavior, artificial intelligence
National Category
Robotics Computer Systems
Research subject
human-computer interaction; Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-198631ISBN: 978-91-7855-768-4 (electronic)ISBN: 978-91-7855-767-7 (print)OAI: oai:DiVA.org:umu-198631DiVA, id: diva2:1687668
Public defence
2022-09-16, NAT.D.360, Naturvetarhuset, Umeå, 13:15 (English)
Opponent
Supervisors
Available from: 2022-08-26 Created: 2022-08-16 Last updated: 2022-08-22Bibliographically approved
List of papers
1. Inference of the Intentions of Unknown Agents in a Theory of Mind Setting
Open this publication in new window or tab >>Inference of the Intentions of Unknown Agents in a Theory of Mind Setting
2021 (English)In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection / [ed] Dignum F., Corchado J.M., De La Prieta F., Springer Science+Business Media B.V., 2021, p. 188-200Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous agents may be required to form an understanding of other agents for which they don’t possess a model. In such cases, they must rely on their previously gathered knowledge of agents, and ground the observed behaviors in the models this knowledge describes by theory of mind reasoning. To give flesh to this process, in this paper we propose an algorithm to ground observations on a combination of priorly possessed Belief-Desire-Intention models, while using rationality to infer unobservable variables. This allows to jointly infer beliefs, goals and intentions of an unknown observed agent by using only available models.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2021
Series
International Conference on Practical Applications of Agents and Multi-Agent Systems, ISSN 03029743, E-ISSN 16113349
Keywords
Belief-desire-intention, Intent recognition, Planning domain description language, Theory of mind, Unknown agent model
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-188638 (URN)10.1007/978-3-030-85739-4_16 (DOI)000791045800016 ()2-s2.0-85116381724 (Scopus ID)9783030857387 (ISBN)
Conference
19th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2021, Salamanca, Spain, October 6-8, 2021.
Note

Series: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), nr. 12946

Available from: 2021-10-20 Created: 2021-10-20 Last updated: 2023-09-05Bibliographically approved
2. Intent Recognition from Speech and Plan Recognition
Open this publication in new window or tab >>Intent Recognition from Speech and Plan Recognition
2020 (English)In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness: The PAAMS Collection / [ed] Yves Demazeau, Tom Holvoet, Juan M. Corchado, Stefania Costantini, Springer, 2020, p. 212-223Conference paper, Published paper (Refereed)
Abstract [en]

In multi-agent systems, the ability to infer intentions allows artificial agents to act proactively and with partial information. In this paper we propose an algorithm to infer a speakers intentions with natural language analysis combined with plan recognition. We define a Natural Language Understanding component to classify semantic roles from sentences into partially instantiated actions, that are interpreted as the intention of the speaker. These actions are grounded to arbitrary, hand-defined task domains. Intent recognition with partial actions is statistically evaluated with several  planning domains. We then define a Human-Robot Interaction setting where both utterance classification and plan recognition are tested using a Pepper robot. We further address the issue of missing parameters in declared intentions and robot commands by leveraging the Principle of Rational Action, which is embedded in the plan recognition phase.

Place, publisher, year, edition, pages
Springer, 2020
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12092
Keywords
Intent Recognition, Plan Recognition, Natural Language Understanding, Semantic Role Labeling, Algorithms
National Category
Robotics
Identifiers
urn:nbn:se:umu:diva-169561 (URN)10.1007/978-3-030-49778-1_17 (DOI)2-s2.0-85088564119 (Scopus ID)978-3-030-49777-4 (ISBN)978-3-030-49778-1 (ISBN)
Conference
18th International Conference on Practical Applications of Agents and Multi-Agent Systems, L'Aquila, Italy, 7-8 October, 2020
Available from: 2020-04-06 Created: 2020-04-06 Last updated: 2022-08-16Bibliographically approved
3. Probabilistic Plan Legibility with Off-the-shelf Planners
Open this publication in new window or tab >>Probabilistic Plan Legibility with Off-the-shelf Planners
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Legible planning is the creation of plans that best disambiguate their goals from a set of other candidates from an observer's perspective. In this paper we propose a method for legible planning for arbitrary PDDL domains, by extending previous research on legibility to classical planning without requiring to construct ad-hoc planners. We also discuss how the observer perspective may  be estimated through a second order theory of mind that connects the planner's and the observer's task spaces. Our solution can for example be deployed in human-robot teaming scenarios, where an autonomous robot in a team can implicitly communicate its goal by producing legible plans. We present benchmark results on several PDDL planning domains. Our results generally show that plan legibility is a trade-off with plan efficiency, however, not all planning domains allows to increase legibility in the same way and a regularizing factor to balance legibility and efficiency was proved necessary.

National Category
Robotics
Identifiers
urn:nbn:se:umu:diva-179025 (URN)
Available from: 2021-01-23 Created: 2021-01-23 Last updated: 2022-08-16
4. Informative communication of robot plans
Open this publication in new window or tab >>Informative communication of robot plans
2022 (English)In: Advances in practical applications of agents, multi-agent systems, and complex systems simulation: the PAAMS collection / [ed] Frank Dignum; Philippe Mathieu; Juan Manuel Corchado; Fernando De La Prieta, Springer, 2022, p. 332-344Conference paper, Published paper (Other academic)
Abstract [en]

When a robot is asked to verbalize its plan it can do it in many ways. For example, a seemingly natural strategy is incremental, where the robot verbalizes its planned actions in plan order. However, an important aspect of this type of strategy is that it misses considerations on what is effectively informative to communicate, because not considering what the user knows prior to explanations. In this paper we propose a verbalization strategy to communicate robot plans informatively, by measuring the information gain that verbalizations have against a second-order theory of mind of the user capturing his prior knowledge on the robot. As shown in our experiments, this strategy allows to understand the robot's goal much quicker than by using strategies such as increasing or decreasing plan order. In addition, following our formulation we hint to what is informative and why when a robot communicates its plan.

Place, publisher, year, edition, pages
Springer, 2022
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13616
Keywords
Bayesian network, Human-robot interaction, Mirror agent model, Plan verbalization
National Category
Robotics
Identifiers
urn:nbn:se:umu:diva-192809 (URN)10.1007/978-3-031-18192-4_27 (DOI)2-s2.0-85141818392 (Scopus ID)9783031181917 (ISBN)9783031181924 (ISBN)
Conference
20th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2022), L'Aquila (Italy), 13-15 July, 2022
Note

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI) 

Available from: 2022-02-28 Created: 2022-02-28 Last updated: 2022-12-15Bibliographically approved
5. Policy regularization for legible behavior
Open this publication in new window or tab >>Policy regularization for legible behavior
2023 (English)In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058, Vol. 35, no 23, p. 16781-16790Article in journal (Refereed) Published
Abstract [en]

In this paper we propose a method to augment a Reinforcement Learning agent with legibility. This method is inspired by the literature in Explainable Planning and allows to regularize the agent’s policy after training, and without requiring to modify its learning algorithm. This is achieved by evaluating how the agent’s optimal policy may produce observations that would make an observer model to infer a wrong policy. In our formulation, the decision boundary introduced by legibility impacts the states in which the agent’s policy returns an action that is non-legible because having high likelihood also in other policies. In these cases, a trade-off between such action, and legible/sub-optimal action is made. We tested our method in a grid-world environment highlighting how legibility impacts the agent’s optimal policy, and gathered both quantitative and qualitative results. In addition, we discuss how the proposed regularization generalizes over methods functioning with goal-driven policies, because applicable to general policies of which goal-driven policies are a special case.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Reinforcement Learning, Transparency, Interpretability, Legibility
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-192813 (URN)10.1007/s00521-022-07942-7 (DOI)000875293700002 ()2-s2.0-85140636891 (Scopus ID)
Note

Originally included in thesis in manuscript form.

Available from: 2022-02-28 Created: 2022-02-28 Last updated: 2023-12-05Bibliographically approved
6. The mirror agent model: a Bayesian architecture for interpretable agent behavior
Open this publication in new window or tab >>The mirror agent model: a Bayesian architecture for interpretable agent behavior
2022 (English)In: Explainable and transparent AI and multi-agent systems: 4th international workshop, EXTRAMAAS 2022, virtual event, may 9–10, 2022, revised selected papers / [ed] Davide Calvaresi; Amro Najjar; Michael Winikoff; Kary Främling, Springer Nature, 2022, p. 111-123Conference paper, Published paper (Other academic)
Abstract [en]

In this paper we illustrate a novel architecture generating interpretable behavior and explanations. We refer to this architecture as the Mirror Agent Model because it defines the observer model, that is the target of explicit and implicit communications, as a mirror of the agent's. With the goal of providing a general understanding of this work, we firstly show prior relevant results addressing the informative communication of agents intentions and the production of legible behavior. In the second part of the paper we furnish the architecture with novel capabilities for explanations through off-the-shelf saliency methods, followed by preliminary qualitative results.

Place, publisher, year, edition, pages
Springer Nature, 2022
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13283
Keywords
Interpretability, Explainability, Bayesian networks, Mirror Agent Model
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-194479 (URN)10.1007/978-3-031-15565-9_7 (DOI)000870042100007 ()2-s2.0-85140488434 (Scopus ID)978-3-031-15564-2 (ISBN)978-3-031-15565-9 (ISBN)
Conference
4th International Workshop on EXplainable and Transparent AI and Multi-Agent Systems, EXTRAAMAS 2022, Virtual event, May 9-10, 2022
Available from: 2022-05-05 Created: 2022-05-05 Last updated: 2022-11-10Bibliographically approved
7. Traveling Drinksman: A mobile service robot for people in care-homes
Open this publication in new window or tab >>Traveling Drinksman: A mobile service robot for people in care-homes
Show others...
2020 (English)In: 52nd International Symposium on Robotics, ISR 2020, VDE Verlag GmbH, 2020, p. 31-36Conference paper, Published paper (Other academic)
Abstract [en]

This paper describes ongoing work on the development of a service robot for serving drinks to people sitting at tables, for example in the recreation room of a care-house. The robot, denoted the Traveling Drinksman, should be able to detect theoccupied tables, navigate safely according to defined policies, and interact with the humans sitting to serve them a drink. We present initial results addressing all of these problems with different sub-modules, including numerical results for the human head detection module.

Place, publisher, year, edition, pages
VDE Verlag GmbH, 2020
Keywords
Service Robotics, Planning, Human Detection
National Category
Robotics
Identifiers
urn:nbn:se:umu:diva-169563 (URN)2-s2.0-85101096683 (Scopus ID)9783800754298 (ISBN)
Conference
52nd International Symposium on Robotics, Online, December 9-10, 2020
Available from: 2020-04-06 Created: 2020-04-06 Last updated: 2022-08-16Bibliographically approved

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Persiani, Michele

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