Generalizing foraging theory for analysis and design

Theodore Pavlic, Kevin M. Passino

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Foraging theory has been the inspiration for several decision-making algorithms for task-processing agents facing random environments. As nature selects for foraging behaviors that maximize lifetime calorie gain or minimize starvation probability, engineering designs are favored that maximize returned value (e.g. profit) or minimize the probability of not reaching performance targets. Prior foraging-inspired designs are direct applications of classical optimal foraging theory (OFT). Here, we describe a generalized optimization framework that encompasses the classical OFT model, a popular competitor, and several new models introduced here that are better suited for some task-processing applications in engineering. These new models merge features of rate maximization, efficiency maximization, and risk-sensitive foraging while not sacrificing the intuitive character of classical OFT. However, the central contributions of this paper are analytical and graphical methods for designing decision-making algorithms guaranteed to be optimal within the framework. Thus, we provide a general modeling framework for solitary agent behavior, several new and classic examples that apply to it, and generic methods for design and analysis of optimal task-processing behaviors that fit within the framework. Our results extend the key mathematical features of optimal foraging theory to a wide range of other optimization objectives in biological, anthropological, and technological contexts.

Original languageEnglish (US)
Pages (from-to)505-523
Number of pages19
JournalInternational Journal of Robotics Research
Volume30
Issue number5
DOIs
StatePublished - Apr 2011
Externally publishedYes

Fingerprint

Foraging
Processing
Decision making
Profitability
Decision Making
Maximise
Minimise
Graphical Methods
Feature Model
Design
Optimization
Random Environment
Model Theory
Engineering Design
Analytical Methods
Profit
Intuitive
Lifetime
Engineering
Target

Keywords

  • Agent-based models
  • Biomimicry
  • Decision making
  • Markov renewal processes
  • Mathematical biology
  • Optimization
  • Solitary agent behavior

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Modeling and Simulation

Cite this

Generalizing foraging theory for analysis and design. / Pavlic, Theodore; Passino, Kevin M.

In: International Journal of Robotics Research, Vol. 30, No. 5, 04.2011, p. 505-523.

Research output: Contribution to journalArticle

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