Generalizing foraging theory for analysis and design

Theodore P. Pavlic, Kevin M. Passino

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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
Issue number5
StatePublished - Apr 2011
Externally publishedYes


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

ASJC Scopus subject areas

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


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