Probabilistic Selection in AgentSpeak(L)

TL;DR

This paper introduces a novel two-layer agent programming framework combining symbolic and probabilistic AI techniques to enhance autonomy in complex, uncertain environments, demonstrated through the GoldMiners example.

Contribution

It presents an innovative, conflict-free hybrid approach integrating symbolic and probabilistic methods for agent programming.

Findings

Framework effectively manages uncertainty in agent behavior.

Demonstrated successful application on GoldMiners example.

Enhances agent autonomy in dynamic environments.

Abstract

Agent programming is mostly a symbolic discipline and, as such, draws little benefits from probabilistic areas as machine learning and graphical models. However, the greatest objective of agent research is the achievement of autonomy in dynamical and complex environments --- a goal that implies embracing uncertainty and therefore the entailed representations, algorithms and techniques. This paper proposes an innovative and conflict free two layer approach to agent programming that uses already established methods and tools from both symbolic and probabilistic artificial intelligence. Moreover, this framework is illustrated by means of a widely used agent programming example, GoldMiners.