When Suboptimal Rules

Elmalech, Avshalom; Sarne, David; Rosenfeld, Avi; Erez, Eden Shalom

doi:10.1609/aaai.v29i1.9335

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…Designers may have bounds and biases, and may not possess complete knowledge. These factors can lead them to behave in ways that appear irrational to the assistant (Elmalech et al 2015). To mitigate the effect of this, the assistant must adapt its choice of interactions to these factors (De Peuter and Kaski 2023).…”

Section: Creating a Collaborative Assistantmentioning

confidence: 99%

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Toward AI assistants that let designers design

2023

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We need to rethink how we assist designers with artificial intelligence (AI). AI should aim to cooperate, not automate, by supporting and leveraging the creativity and problem-solving capabilities of designers. The challenge for such AI is how to infer designers' goals and then help them without being needlessly disruptive. We introduce AI-assisted design, a new framework for creating such AI, built around generative user models, which allow it to infer and adapt to designers' goals, reasoning, and capabilities.

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Section: Creating a Collaborative Assistantmentioning

confidence: 99%

“…We will call these behavioral factors. It is important that these are accounted for and inferred jointly with the designer's goals (De Peuter and Kaski 2023;Elmalech et al 2015). For example, an anchoring bias can cause a designer to fixate on their current design and refuse large changes proposed by an assistant.…”

Section: Introductionmentioning

confidence: 99%

Toward AI assistants that let designers design

2023

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“…Sarne et al (2011) have shown that users' performance can be substantially improved through manipulating the input (for example, the information concerning the different choices) that they receive. Elmalech et al (2015) suggest an approach according to which the decision regarding the advice to be provided should not be made merely based on the encapsulated utility, but rather also based on the likelihood of its acceptance by the user. Das, Mathieu, and Ricketts (2009) theoretically analyze a recommender system that tries to maximize its own expect-ed utility.…”

Section: Related Workmentioning

confidence: 99%

“…Elmalech et al (2015) suggest an approach according to which the decision regarding the advice to be provided should not be made merely based on the encapsulated utility, but rather also based on the likelihood of its acceptance by the user. Das, Mathieu, and Ricketts (2009) theoretically analyze a recommender system that tries to maximize its own expected utility.…”

Section: Related Workmentioning

confidence: 99%

Advice Provision for Energy Saving in an Automobile Climate‐Control System

et al. 2015

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Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. The need to save energy becomes even greater when considering an electric car, since heavy use of the climate control system may exhaust the battery. In this article we consider a method for an automated agent to provide advice to drivers which will motivate them to reduce the energy consumption of their climate control unit. Our approach takes into account both the energy consumption of the climate control system and the expected comfort level of the driver. We therefore build two models, one for assessing the energy consumption of the climate control system as a function of the system’s settings, and the other, models human comfort level as a function of the climate control system’s settings. Using these models, the agent provides advice to the driver considering how to set the climate control system. The agent advises settings which try to preserve a high level of comfort while consuming as little energy as possible. We empirically show that drivers equipped with our agent which provides them with advice significantly save energy as compared to drivers not equipped with our agent.

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“…Recent progress that has been made in the development of automated agents which can interact proficiently with people, including negotiation agents (Rosenfeld et al 2014b;Dsouza et al 2013;Vahidov, Kersten, and Saade 2012;de Melo, Carnevale, and Gratch 2011;Gal et al 2012;Oshrat, Lin, and Kraus 2009;Lin et al 2008), advice and service provision agents (Azaria et al 2012;Delecroix, Morge, and Routier 2013;Elmalech et al 2015), automated mediators (Lin, Gev, and Kraus 2011), discussion agents (Fenster, Zuckerman, and Kraus 2012;Ren et al 2014) and persuasion agents (Azaria, Aumann, and Kraus 2014), can be the basis for the development of agents capable of performing all four tasks. We will discuss a few ideas and difficulties in relation to such agents in this extended abstract.…”

Section: Encouragementmentioning

confidence: 99%

Intelligent Agents for Rehabilitation and Care of Disabled and Chronic Patients

Kraus

2015

AAAI

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The number of people with disabilities is continuously increasing. Providing patients who have disabilities with the rehabilitation and care necessary to allow them good quality of life creates overwhelming demands for health and rehabilitation services. We suggest that advancements in intelligent agent technology provide new opportunities for improving the provided services. We will discuss the challenges of building an agent for the health care domain and present four capabilities that are required for an agent in the health care domain: planning, monitoring, intervention and encouragement. We will discuss the importance of personalizing all of them and the needto facilitate cooperation between the automated agent and the human care givers. We will review recent technology that can be used toward the development of agents that can have these capabilities and their promise in automating services such as physiotherapy, speech therapy and cognitive training.

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When Suboptimal Rules

Cited by 7 publications

References 21 publications

Toward AI assistants that let designers design

Toward AI assistants that let designers design

Advice Provision for Energy Saving in an Automobile Climate‐Control System

Intelligent Agents for Rehabilitation and Care of Disabled and Chronic Patients

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