The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Thomas A Waite - One of the best experts on this subject based on the ideXlab platform.
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Foraging Theory for autonomous vehicle decision making system design
Journal of Intelligent and Robotic Systems, 2007Co-Authors: B W Andrews, K M Passino, Thomas A WaiteAbstract:Foraging Theory is typically used to model animal decision making. We describe an agent such as an autonomous vehicle or software module as a forager searching for tasks. The prey model is used to predict which types of tasks an agent should choose to maximize its rate of reward, and the patch model is used to predict when an agent should leave a patch of tasks and how to choose within-patch search patterns. We expand and apply these concepts to fit an autonomous vehicle control problem and to provide insight into how to make high-level control decisions. We also discuss extensions of the basic models, showing how a risk-sensitive version can be used to alter policies when time or fuel is limited. Throughout the applications, we examine ways an agent can estimate environmental parameters when such parameters are not known.
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Social Foraging Theory for robust multiagent system design
IEEE Transactions on Automation Science and Engineering, 2007Co-Authors: Burton W. Andrews, Kevin M. Passino, Thomas A WaiteAbstract:An analogy between an agent (e.g., an autonomous vehicle) and a biological forager is extended to a social environment by viewing a communication network as implementing interagent sociality. We first describe engineering design within an evolutionary game-theoretic framework. We then explain why sociality may emerge in some environments and for some agent objectives. Next, we derive the evolutionarily stable design strategy for an agent manufacturer: 1) choosing whether the agent it produces should cooperate with other agents in a search problem and 2) choosing the group size of a multiagent system tasked with a cooperative search problem. We show the impact of "agent relatedness," a measure of common descent between two agents based on their underlying manufacturers, on the choices in scenarios 1) and 2). Our predictions are evaluated in an autonomous vehicle simulation testbed. The results illustrate a new methodology for manufacturers to make robust, optimal choices for multiagent system design for a given set of objectives and domain of operation. Note to Practitioners-The design of autonomous multirobot systems with various applications, such as in parts production or search and destroy operations in a military environment, is of growing importance. Here, we integrate economic and technical issues into an unified engineering design framework for the manufacturers of robots. Our approach leads to manufacturer design decisions that are robust relative to the market for a manufacturer's products. Robot component aspects, such as sensors and communications as well as mission performance aspects, can be captured and coupled into the design process. We use the design of intervehicle cooperation and robot group size to illustrate this approach. The practical significance lies in the fact that we take a broad perspective on engineering design, one closer to the real world, due to the considerations of marketplace economics. Moreover, the approach provides a framework-\n to study design choices that escape systematic analysis in other frameworks (e.g., group size)
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Foraging Theory for decision-making system design: Task-type choice
Proceedings of the IEEE Conference on Decision and Control, 2004Co-Authors: Burton W. Andrews, Kevin M. Passino, Thomas A WaiteAbstract:Foraging Theory is typically used to model animal decision making. We describe an agent such as an autonomous vehicle or software module as a forager searching for tasks. The prey model is used to predict which types of tasks an agent should choose to maximize its rate of reward. We expand and apply these concepts to fit an autonomous vehicle control problem and to provide insight into how to make high-level control decisions. We also discuss extensions of the basic prey model, showing how a risk-sensitive version can be used to alter policies when time or fuel is limited. Throughout the applications, we examine ways an agent can estimate environmental parameters when such parameters are not known.
Graham H. Pyke - One of the best experts on this subject based on the ideXlab platform.
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Optimal Foraging Theory
Encyclopedia of Social Insects, 2020Co-Authors: Graham H. Pyke, Christopher K. StarrAbstract:All of life forages for resources that are needed for survival, development, and reproduction. Optimal Foraging Theory (OFT) aims to understand Foraging behavior by hypothesizing that animals forage...
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plant pollinator co evolution it s time to reconnect with optimal Foraging Theory and evolutionarily stable strategies
Perspectives in Plant Ecology Evolution and Systematics, 2016Co-Authors: Graham H. PykeAbstract:Abstract Pollination syndromes (correlations between floral and pollinator traits), have long interested ecologists, but remain inadequately explained. For example, plant species pollinated by relatively large animals cannot have evolved correspondingly high rates of nectar-energy production simply because such animals need relatively more energy; evolution does not work that way. The inverse correlation between pollinator body-size and nectar concentration is similarly difficult to explain. To remedy this, I propose that Optimal Foraging Theory (OFT) and the Evolutionarily Stable Strategy approach (ESS) be combined and applied to pollination syndromes. Both hypothesise that, through evolution, average biological fitness of individuals has been maximised. OFT predicts Foraging consequences for pollinators varying in body size, and other attributes, allowing the ESS approach to be applied to co-adapted plant–pollinator traits. This should lead to predicted relationships between plants and their pollinators. The steps involved in this process are conceptually straightforward, but empirically difficult, which may explain why the approach has been very little pursued in the past. However such difficulties can be overcome, thus pointing to the future. We surely need to understand pollination systems, in order to conserve and manage them. It is therefore time to reconnect OFT and plant–pollinator co-evolution, within the general ESS approach, and hence increasing our understanding of pollination syndromes and other plant–pollinator relationships.
John M Mcnamara - One of the best experts on this subject based on the ideXlab platform.
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clarifying the relationship between prospect Theory and risk sensitive Foraging Theory
Evolution and Human Behavior, 2014Co-Authors: Alasdair I Houston, Tim W Fawcett, Dave E W Mallpress, John M McnamaraAbstract:Abstract When given a choice between options with uncertain outcomes, people tend to be loss averse and risk averse regarding potential gains and risk prone regarding potential losses. These features of human decision making are captured by prospect Theory (PT)—a hugely influential descriptive model of choice, but one which lacks any unifying principle that might explain why such preferences exist. Recently there have been several attempts to connect PT with risk-sensitive Foraging Theory (RSFT), a normative framework developed by evolutionary biologists to explain how animals should choose optimally when faced with uncertain Foraging options. Although this seems a promising direction, here we show that current approaches are overly simplistic, and, despite their claims, they leave key features of PT unaccounted for. A common problem is the failure to appreciate the central concept of reproductive value in RSFT, which depends on the decision maker's current state and the particular situation it faces. Reproductive value provides a common currency in which decisions can be compared in a logical way. In contrast, existing models provide no rational justification for the reference state in PT. Evolutionary approaches to understanding PT preferences must confront this basic problem.
Burton W. Andrews - One of the best experts on this subject based on the ideXlab platform.
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Social Foraging Theory for robust multiagent system design
IEEE Transactions on Automation Science and Engineering, 2007Co-Authors: Burton W. Andrews, Kevin M. Passino, Thomas A WaiteAbstract:An analogy between an agent (e.g., an autonomous vehicle) and a biological forager is extended to a social environment by viewing a communication network as implementing interagent sociality. We first describe engineering design within an evolutionary game-theoretic framework. We then explain why sociality may emerge in some environments and for some agent objectives. Next, we derive the evolutionarily stable design strategy for an agent manufacturer: 1) choosing whether the agent it produces should cooperate with other agents in a search problem and 2) choosing the group size of a multiagent system tasked with a cooperative search problem. We show the impact of "agent relatedness," a measure of common descent between two agents based on their underlying manufacturers, on the choices in scenarios 1) and 2). Our predictions are evaluated in an autonomous vehicle simulation testbed. The results illustrate a new methodology for manufacturers to make robust, optimal choices for multiagent system design for a given set of objectives and domain of operation. Note to Practitioners-The design of autonomous multirobot systems with various applications, such as in parts production or search and destroy operations in a military environment, is of growing importance. Here, we integrate economic and technical issues into an unified engineering design framework for the manufacturers of robots. Our approach leads to manufacturer design decisions that are robust relative to the market for a manufacturer's products. Robot component aspects, such as sensors and communications as well as mission performance aspects, can be captured and coupled into the design process. We use the design of intervehicle cooperation and robot group size to illustrate this approach. The practical significance lies in the fact that we take a broad perspective on engineering design, one closer to the real world, due to the considerations of marketplace economics. Moreover, the approach provides a framework-\n to study design choices that escape systematic analysis in other frameworks (e.g., group size)
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Foraging Theory for multizone temperature control
IEEE Computational Intelligence Magazine, 2006Co-Authors: Nicanor Quijano, Kevin M. Passino, Burton W. AndrewsAbstract:Models from behavioral ecology, specifically Foraging Theory, are used to describe the decisions an animal forager must make in order to maximize its rate of energy gain and thereby improve its survival probability. Using a bioinspired methodology, we view an animal as a software agent, the Foraging landscape as a spatial layout of temperature zones, and nutrients as errors between the desired and actual temperatures in the zones. Then, using Foraging Theory, we define a decision strategy for the agent that has an objective of reducing the temperature errors in order to track a desired temperature. We describe an implementation of a multizone temperature experiment, and show that the use of multiple agents defines a distributed controller that can equilibrate the temperatures in the zones in spite of interzone, ambient, and network effects. We discuss relations to ideas from theoretical ecology, and identify a number of promising research directions. It is our hope that the results of this paper will motivate other research on bioinspired methods based on behavioral ecology
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Foraging Theory for decision-making system design: Task-type choice
Proceedings of the IEEE Conference on Decision and Control, 2004Co-Authors: Burton W. Andrews, Kevin M. Passino, Thomas A WaiteAbstract:Foraging Theory is typically used to model animal decision making. We describe an agent such as an autonomous vehicle or software module as a forager searching for tasks. The prey model is used to predict which types of tasks an agent should choose to maximize its rate of reward. We expand and apply these concepts to fit an autonomous vehicle control problem and to provide insight into how to make high-level control decisions. We also discuss extensions of the basic prey model, showing how a risk-sensitive version can be used to alter policies when time or fuel is limited. Throughout the applications, we examine ways an agent can estimate environmental parameters when such parameters are not known.
Michael D Oseen - One of the best experts on this subject based on the ideXlab platform.
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context dependent risk sensitive Foraging preferences in wild rufous hummingbirds
Animal Behaviour, 1999Co-Authors: Andrew T Hurly, Michael D OseenAbstract:Abstract We tested the risk-sensitive Foraging preferences of wild rufous hummingbirds, Selasphorus rufus , with three types of artificial flowers. All three flower types provided the same mean volume of 30 μl of sucrose, but differed in terms of variability of the reward: constant, low variance and high variance. In trinary comparisons, subjects preferred the low-variance reward over the constant reward, and the constant reward over the high-variance reward; a result not predicted by risk-sensitive Foraging Theory. However, when tested with traditional binary comparisons, hummingbirds showed conventional risk-averse behaviour and selected the constant reward over the low- or high-variance rewards. This reversal of preference represents a context-dependent Foraging preference. The utility of selecting intermediate levels of risk and the source of the preference reversal are discussed relative to risk-sensitive Foraging Theory and the effects of local context on Foraging choices.