Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

Citation classic - optimal foraging - a selective review of theory and tests

https://link.springer.com/content/pdf/10.1007/s12525-022-00537-z.pdf

Artificial intelligence in E-Commerce: a bibliometric study and literature review

Navigation systems for runners commonly provide turn-by-turn directions via voice and/or map-based visualizations. While voice directions require permanent attention, map-based guidance requires regular consultation. Both disrupt the running activity. To address this, we designed RunAhead, a navigation system using head scanning to query for navigation feedback, and we explored its suitability for runners in an outdoor experiment. In our design, we provide the runner with simple and intuitive navigation feedback on the path s/he is looking at through three different feedback modes: haptic, music and audio cues. In our experiment, we compare the resulting three versions of RunAhead with a baseline voice-based navigation system. We find that demand and error are equivalent across all four conditions. However, the head scanning based haptic and music conditions are preferred over the baseline and these preferences are impacted by runners' habits. With this study we contribute insights for designing navigation support for runners.

RunAhead: Exploring Head Scanning based Navigation for Runners

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired counter-effects on users, items, producers, platforms, or even the society at large, such as compromised user trust due to non-transparency, unfair treatment of different consumers, or producers, privacy concerns due to extensive use of user's private data for personalization, just to name a few. All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks. In this survey, we will introduce techniques related to trustworthy and responsible recommendation, including but not limited to explainable recommendation, fairness in recommendation, privacy-aware recommendation, robustness in recommendation, user controllable recommendation, as well as the relationship between these different perspectives in terms of trustworthy and responsible recommendation. Through this survey, we hope to deliver readers with a comprehensive view of the research area and raise attention to the community about the importance, existing research achievements, and future research directions on trustworthy recommendation.

A Survey on Trustworthy Recommender Systems

In this paper, we report the results from a Hybrid Wizard of Oz experiment consisting of a critical incident interview and an in-situ simulation. The study aimed at validating the need for a contextualised and personalised Point-Of-Interest (POI) recommender and understanding the detailed user needs for it. Our key findings include: feeling bored as a key trigger to search for POIs, trust issues with the existing recommendation sources, intent to find free activities, information needs on areas-of-interest beyond points-of-interest, support for socialising, and language barriers. With this study, we also exemplify a cost- and time-effective approach for design of intelligent systems.

Hybrid Wizard of Oz: Concept Testing a Recommender System

We propose Ageing Clouds as a concept for urban exploration invoking freedom in navigation and strategy in planning. Wandering bound to a handheld smartphone is an obvious obstruction to the experience of explorers. Our approach is to construct a visual aid on mobile devices that roughly supports navigation without demanding continuous user attention. The interesting urban neighbourhoods of a city are represented as clouds on a mobile map. Post-visit, these clouds age and turn grey in the regions explored by the user. We present our initial steps to evolve mobile maps from rigid navigation to support unconstrained yet strategic urban exploration.

Ageing Clouds: Novel yet Natural Support for Urban Exploration

We demonstrate SIMURGH, an interactive framework for generating customized travel packages (TPs) for individuals or for groups of travelers. This is beneficial in various use cases such as tourism planning and advertisement. Simurgh relies on gathering preferences of travelers and solving an optimization problem to generate personalized travel packages. SIMURGH goes beyond personalization by allowing travelers to customize travel packages via simple-yet-powerful interaction operators.

Interactive Generation and Customization of Travel Packages for Individuals and Groups

This poster describes a fundamental user research to uncover the challenges and opportunities for a conversational recommender system. The study was conducted in three different environments: home, work and street. During the sessions, the participants (i) recounted their previous experiences of interacting with conversational agents in a critical incident interview, (ii) performed a task to explore restaurant recommendations with Google Assistant, and (iii) discussed their views on conversational recommendations. We organize the challenges uncovered into three levels: environment, conversation, and system. Further, we illustrate the identified opportunities of whispering interaction, collaborative recommendations, driving scenario, and local-like conversations for delivering recommendations.

What is Natural?: Challenges and Opportunities for Conversational Recommender Systems

Recommender systems are widely integrated into our everyday activities. These intelligent systems succeed in learning the user's profile to recommend movies, music, news and more. However, for designing context-aware recommendations, new challenges emerge in predicting the situational needs of the user. We prototyped Ambient Wanderer, our personalised and contextualised Point-of-Interest (POI) recommender system and experimented it with new locals, people who have recently relocated to a city. Our key findings include: sudden breakdowns during urban exploration, trust issues with the recommendations from people unlike them, feeling bored as the trigger to POI search, intent to find free activities, information needs on areas-of-interest beyond points-of-interest and the demand to build a new social life. For each of these needs, we present the implications to design mobile recommendations for urban exploration.

Sruthi Viswanathan

Papers

Ageing Clouds: Novel yet Natural Support for Urban Exploration

Interactive Generation and Customization of Travel Packages for Individuals and Groups

Hybrid Wizard of Oz: Concept Testing a Recommender System

What is Natural?: Challenges and Opportunities for Conversational Recommender Systems

Designing Ambient Wanderer: Mobile Recommendations for Urban Exploration