Model-based agents

- Let's get into model-based agents, which are more competent because they maintain an internal representation of the world and use it to simulate different scenarios before making decisions or taking action. These agents consider multiple actions and predict the outcome of those actions. An example would be a vacuum cleaner that has sensors to detect dirt and obstacles and uses a model of the room to plan its cleaning session. Model-based agents typically do planning, forecasting, and optimization. For example, they may analyze customer purchases and market signals to forecast demand or plan a marketing campaign. In a healthcare setting, the agents may understand a patient's multiple health indicators and develop a healthcare treatment plan. Other examples include credit scoring or loan approvals in financial services, as well as the many robo-advisors providing goal-based financial planning for individuals. They can also provide employee performance predictions within organizations. So what's in common in all of these use cases? It's the ability of the model-based agent to use an internal representation or simulation of the environment to predict the outcomes of different approaches and optimize for the desired outcome or goal. That internal model allows them to simulate different scenarios and then optimize a course of action to avoid undesirable outcomes. They can also adapt quickly to changes or new environments by updating their internal models with new information. This allows them to respond in dynamic situations and adjust their approach as needed. This means that these models don't need to be trained on a large data set, rather they benefit most from a smaller, more accurate model for training. Just like simple reflex agents, there are limitations, namely that building that accurate complex model can be time-consuming and difficult. If a model isn't sufficiently complex and accurate, the agent may not be able to generalize beyond the model. And because it's so dependent on an accurate model, these agents may struggle with highly dynamic or unpredictable environments. Lastly, because the model-based agents run multiple simulations and scenarios, it can be computationally complex, requiring significant processing resources and power. Model-based agents may be a good option for your organization if your competitive advantage, offerings, or services rely heavily on optimizing routine scenarios or planning for specific goals. They can shorten the time needed to create and optimize a plan. Now the next agent type we'll explore builds upon model-based agents. Join me as we explore goal-based agents.