It was a simpler time. A friend introduced us, pulling up a static yellow webpage using a shaky dial-up modem. A man stood forth, dressed in a dapper black pinstriped suit with a red-accented tie. He held one hand out, as if carrying an imaginary waiter's tray. He looked regal and confident and eminently at my service.

On August 8th, 2021, a team of four graduate students from the University of Toronto presented their ethical design in the world's first ever roboethics competition, the RO-MAN 2021 Roboethics to Design & Development Competition. During the competition, design teams tackled a challenging yet relatable scenario--introducing a robot helper to the household. The students' solution, entitled "Jeeves, the Ethically Designed Interface (JEDI)", demonstrated how home robots can act safely and according to social and cultural norms. Click here to watch their video submission. JEEVES acted as an extension of the mother and the interface rules accommodated her priorities.

Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description. Since a scenario contains both keyphrases for retrieval and much noise, retrieval for SQA is extremely difficult. Moreover, it can hardly be supervised due to the lack of relevance labels of paragraphs for SQA. To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. JEEVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.

JEEVES's success depended crucially on JEEVES's visual range was extremely JEEVES as successful as it was? JEEVES's success was that its software JEEVES's hardware was designed and built by JEEVES can reverse the direction of the brush. It is equipped with seven ultrasonic proximity sensors (only five were used in the competition), a wide-angle color camera, and a high-speed colorbased vision system manufactured by Newton Research Labs. Prior to the competition, the vision system was trained to recognize yellow tennis balls, pink squiggle balls, and cyan markers that marked the gate. The vision system proved extremely reliable during the competition, benefiting from clear color cues provided by the objects.

The original idea behind Ask Jeeves was to provide answers to questions posed in everyday language. "Jeeves" was the name of your very own personal assistant, or butler, fetching answers to any question you asked. In a time where the internet was still very much in its infancy, the prospect really sparked my imagination. Excited like many others after seeing the advert, I turned on my PC to ask Jeeves a question. Although I don't remember the first question I asked, I do remember the disappointment I had when the answer was just a list of websites related to keywords in my search criteria.

This article describes JEEVES, one of the winning entries in the 1996 Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence. JEEVES tied for first place in the finals of the competition after it won both preliminary trials. A key aspect in JEEVES's software design was the ability to acquire a model of the environment. The model, a geometric map constructed from sensory data gathered while the robot performed its task, enabled JEEVES to sweep the arena efficiently. It facilitated the retrieval of balls and their delivery at the gate, and it helped to avoid unintended collisions with obstacles. This article argues that JEEVES's success depended crucially on the existence of the model. It also argues that models are generally useful in mobile robotics -- even in tasks as simple as the one faced in this competition.