| 1. |
- Tomic, Stevan, 1981-
(författare)
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Human Norms for Robotic Minds
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Interactions within human societies are usually regulated by social norms. When humans fail to cope with norms, their behavior may be perceived as antisocial, amusing, provocative, dumb, or even uncanny, depending on the intention ascribed to the actor by the observer. Robots cannot yet follow social norms, and often their behavior is judged in such a manner. We claim that acceptance of robots into human societies will critically depend on their ability to represent, reason about, and learn social norms. How to provide these abilities is the main problem addressed in the thesis.We formalize the notion of social context as an institution that encapsulates a set of abstract social norms. Then, we connect these abstract norms to physical execution through an explicit notion of grounding, that puts together three levels of representation: (1) a formalization of normative structures in human-readable terms, (2) a mapping of these formal structures to models of execution in the physical world, and (3) a vector space representation of all these elements suitable for machine learning algorithms. Given this background, we identify computational problems central to reasoning and learning with norms, and provide solutions to several of these problems.We start by considering two reasoning problems: verification, i.e., how to verify whether or not a physical execution (or its interpretation) adheres to a set of norms; and planning, i.e., how to generate plans adherent to norms. We address these problems by reducing them to known problems with known solutions. Specifically, the verification problem is reduced to a constraint satisfaction problem (CSP), which, in turn, allows us to address planning as a meta-CSP.We then address two problems related to learning with norms: how to learn policies that generate adherent trajectories; and how to re-use learned policies across physical domains. To do so, we translate the elements of our framework into a vector-space format. We address the former problem by using norm verification mechanisms to guide a reinforcement learning agent. To address the latter problem we break it into two sub-problems: interpreting the current situation in a context, and performing the actual (reinforcement) learning. That puts us in a unique position of combining reasoning and learning by searching in the space of groundings and re-usable policies.We evaluate all our solutions with use-case experiments, both on real robots and on simulated agents. Exploring the concepts of abstraction and of interpretation in physical execution touches upon some general questions about intelligence and computation, hence we complement the technical contributions with a discussion of our work from the perspective of Cognitive Science.
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| 2. |
- Tomic, Stevan, 1981-, et al.
(författare)
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Learning Normative Behaviors through Abstraction
- 2020
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Ingår i: ECAI 2020. - : IOS Press. - 9781643681009 - 9781643681016 ; , s. 1547-1554
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Konferensbidrag (refereegranskat)abstract
- Future robots should follow human social norms to be useful and accepted in human society. In this paper, we show how prior knowledge about social norms, represented using an existing normative framework, can be used to (1) guide reinforcement learning agents towards normative policies, and (2) re-use (transfer) learned policies in novel domains. The proposed method is not dependent on a particular reinforcement learning algorithm and can be seen as a means to learn abstract procedural knowledge based on declarative domain-independent semantic specifications.
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| 3. |
- Tomic, Stevan, 1981-, et al.
(författare)
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Too cool for school : adding social constraints in human aware planning
- 2014
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Ingår i: CogRob 2014.
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Konferensbidrag (refereegranskat)abstract
- Robots operating in the presence of humans should adapt their plans and behavior accordingly. The recent area of human-aware planning (HAP) addresses this problem. In this paper, we propose a method for extending HAP to so-called Socially Aware planning. We build on known principles for human context recognition, extending them to support different social situations. In this new paradigm, we are able to define social norms and rules which are taken into account by the planning mechanism to obtain plans consisting of socially adjusted behaviors.
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| 4. |
- Tomic, Stevan, 1981-, et al.
(författare)
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Towards Institutions for Mixed Human-Robot Societies
- 2018
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Ingår i: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, (AAMAS' 18). - : Association for Computing Machinery (ACM). - 9781450356497 ; , s. 2216-2217
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Konferensbidrag (refereegranskat)abstract
- We report an exploration into normative reasoning for robots in human societies using the concept of institutions.
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| 5. |
- Wasik, Alicja, et al.
(författare)
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Towards Norm Realization in Institutions Mediating Human-Robot Societies
- 2018
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Ingår i: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE. - 9781538680940 - 9781538680957 ; , s. 297-304
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Konferensbidrag (refereegranskat)abstract
- Social norms are the understandings that govern the behavior of members of a society. As such, they regulate communication, cooperation and other social interactions. Robots capable of reasoning about social norms are more likely to be recognized as an extension of our human society. However, norms stated in a form of the human language are inherently vague and abstract. This allows for applying norms in a variety of situations, but if the robots are to adhere to social norms, they must be capable of translating abstract norms to the robotic language. In this paper we use a notion of institution to realize social norms in real robotic systems. We illustrate our approach in a case study, where we translate abstract norms into concrete constraints on cooperative behaviors of humans and robots. We investigate the feasibility of our approach and quantitatively evaluate the performance of our framework in 30 real experiments with user-based evaluation with 40 participants.
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