Probabilistic Selection in AgentSpeak(L)
This work addresses the problem of improving agent autonomy for researchers in AI and agent systems, but it appears incremental as it builds on established methods without claiming major breakthroughs.
The paper tackles the challenge of integrating probabilistic methods into symbolic agent programming to enhance autonomy in dynamic environments, proposing a two-layer approach that combines symbolic and probabilistic AI techniques and demonstrating it with the GoldMiners example.
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.