Utility-Based Agent System

A Utility-Based Agent System is an optimization-driven value-maximizing goal-based agent system that selects actions based on utility functions.

• AKA: Utility-Maximizing Agent, Rational Agent System, Value-Based Decision Agent.
• Context:
  • It can typically calculate Expected Utility through probability assessments.
  • It can typically compare Action Outcomes using utility measures.
  • It can typically optimize Decision Making via utility maximization.
  • It can typically handle Uncertainty through probabilistic reasoning.
  • It can typically balance Trade-offs using preference weights.
  • ...
  • It can often incorporate Risk Assessment into utility calculations.
  • It can often adapt Utility Functions based on learned preferences.
  • It can often consider Long-Term Consequences in utility evaluations.
  • It can often integrate Multiple Objectives through composite utility functions.
  • ...
  • It can range from being a Simple Utility-Based Agent System to being a Complex Utility-Based Agent System, depending on its utility-based agent system function sophistication.
  • It can range from being a Single-Attribute Utility Agent System to being a Multi-Attribute Utility Agent System, depending on its utility-based agent system evaluation dimensions.
  • It can range from being a Myopic Utility Agent System to being a Forward-Looking Utility Agent System, depending on its utility-based agent system temporal scope.
  • It can range from being a Risk-Neutral Utility Agent System to being a Risk-Aware Utility Agent System, depending on its utility-based agent system risk consideration.
  • ...
  • It can implement Decision Theory for rational choices.
  • It can utilize Game Theory for strategic interactions.
  • It can employ Markov Decision Processes for sequential decisions.
  • It can leverage Monte Carlo Methods for utility estimation.
  • It can incorporate Bayesian Networks for uncertainty modeling.
  • ...
• Example(s):
  • Economic Utility-Based Agent Systems, such as:
    • Algorithmic Trading Agent maximizing portfolio returns.
    • Auction Bidding Agent optimizing purchase values.
    • Dynamic Pricing Agent balancing revenue and demand.
  • Resource Management Utility-Based Agent Systems, such as:
    • Cloud Resource Allocator optimizing cost-performance ratios.
    • Energy Grid Manager balancing supply-demand utilitys.
    • Network Traffic Controller maximizing throughput utility.
  • Healthcare Utility-Based Agent Systems, such as:
    • Treatment Selection Agent maximizing patient outcome utility.
    • Hospital Resource Scheduler optimizing care delivery utility.
    • Drug Discovery Agent maximizing therapeutic benefit.
  • ...
• Counter-Example(s):
  • Rule-Based Agent System, which follows fixed rules without utility optimization.
  • Random Decision Agent, which selects actions without value consideration.
  • Satisficing Agent, which seeks adequate solutions rather than optimal outcomes.
• See: Goal-Based Agent System, Utility Function, Decision Theory, Rational Agent, Expected Utility Theory, Markov Decision Process, Game Theory, Agent Autonomy Spectrum.

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