Action graphs for proactive robot assistance in smart environments

Harman, Helen; Simoens, Pieter

doi:10.3233/ais-200556

Cited by 12 publications

(12 citation statements)

References 30 publications

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“…The program flow is as follows: (i) action by the human is observed; (ii) next actions are predicted; (iii) predicted actions are mapped to a goal state; (iv) a plan for the robot and a plan for the human are created to reach the goal state; (v) the robot decides which action it should perform by comparing the robot's and the human's plan. The work presented in this paper shares some traits with the one by Harman and Simoens (2020): in both cases we reason on the human's intentions, and make predictions about future states using action models. In our case, however, predictions are made on how the system evolves with and without robot actions, and proactive actions are taken by comparing those predictions.…”

Section: From Intention Recognition To Proactivitymentioning

confidence: 93%

“…This can result in the agent acting now or later or not at all. Harman and Simoens (2020) aim at predicting what action a human is likely to perform next, based on previous actions observed through pervasive sensors in a smart environment. Predictions can enable a robot to proactively assist humans by autonomously executing an action on their behalf.…”

Section: From Intention Recognition To Proactivitymentioning

confidence: 99%

“…Importantly, in our case these actions might not be part of the human's plan. Finally, the trigger to perform proactivity reasoning in Harman and Simoens (2020) is human action, while in our work this trigger is any state change, be it caused by human action or by the environment.…”

Section: From Intention Recognition To Proactivitymentioning

confidence: 99%

See 2 more Smart Citations

Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futures

Buyukgoz¹,

Grosinger²,

Chétouani³

et al. 2022

Preprint

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Robots sharing their space with humans need to be proactive in order to be helpful. Proactive robots are able to act on their own initiative in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize human's intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forcasted. In this paper, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to take into account a broader variety of aspects that are needed for proactivity.

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Section: From Intention Recognition To Proactivitymentioning

confidence: 93%

Section: From Intention Recognition To Proactivitymentioning

confidence: 99%

See 1 more Smart Citation

Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futures

Buyukgoz¹,

Grosinger²,

Chétouani³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This can result in the agent acting now or later or not at all. Harman and Simoens (2020) aimed at predicting what action a human is likely to perform next, based on previous actions observed through pervasive sensors in a smart environment. Predictions can enable a robot to proactively assist humans by autonomously executing an action on their behalf.…”

Section: From Intention Recognition To Proactivitymentioning

confidence: 99%

Two ways to make your robot proactive: Reasoning about human intentions or reasoning about possible futures

et al. 2022

View full text Add to dashboard Cite

Robots sharing their space with humans need to be proactive to be helpful. Proactive robots can act on their own initiatives in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize human intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forecast. In this article, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to consider a broader variety of aspects that are required for proactivity.

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“…It can naturally be represented as a direct graph with nodes representing the agents and their actions, and directed edges linking agents and actions, representing the agents' goals and the ways that they can achieve them. Dependence networks have been applied to several domains including smart environments [30] and cyber-physical Systems [31].…”

Section: Dependence Network and Coalition Formationmentioning

confidence: 99%

Contextual and Possibilistic Reasoning for Coalition Formation

Bikakis

Caire

2020

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In multi-agent systems, agents often need to cooperate and form coalitions to fulfil their goals, for example by carrying out certain actions together or by sharing their resources. In such situations, some questions that may arise are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents’ abilities to carry out certain tasks? In this article, we address the question of how to identify and evaluate the potential agent coalitions, while taking into consideration the uncertainty around the agents’ actions. Our methodology is the following: We model multi-agent systems as Multi-Context Systems, by representing agents as contexts and the dependencies among agents as bridge rules. Using methods and tools for contextual reasoning, we compute all possible coalitions with which the agents can fulfil their goals. Finally, we evaluate the coalitions using appropriate metrics, each corresponding to a different requirement. To demonstrate our approach, we use an example from robotics.

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Action graphs for proactive robot assistance in smart environments

Cited by 12 publications

References 30 publications

Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futures

Two ways to make your robot proactive: reasoning about human intentions, or reasoning about possible futures

Two ways to make your robot proactive: Reasoning about human intentions or reasoning about possible futures

Contextual and Possibilistic Reasoning for Coalition Formation

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