Situated approach (artificial intelligence)

See also: situated, Nouvelle AI, and Behavior-based_AI

The two ways to AI

Classically, a software entity is defined as a simulated element, able to act on itself and on its environment, and which has an internal representation of itself and of the outside world. An entity can communicate with other entities, and its behavior is the consequence of its perceptions, its representations, and its interactions with the other entities.

Simulating entities in a virtual environment requires simulating the entire process that goes from a perception of the environment, or more generally from a stimulus, to an action on the environment. This process is called the AI loop (see figure), and technology used to simulate it can be subdivided in two categories. Sensorimotor or low-level AI deals with either the perception problem (what is perceived?) or the animation problem (how actions are executed?). Decisional or high-level AI deals with the action selection problem (what is the most appropriate action in response to a given perception, i.e. what is the most appropriate behavior?).

Traditional or symbolic AI

There are two main approaches in decisional AI. The vast majority of the technologies available on the market, such as planning algorithms, finite state machines (FSA), or expert systems, are based on the traditional or symbolic AI approach. Its main characteristics are:

• It is top-down: it subdivides, in a recursive manner, a given problem into a series of sub-problems that are supposedly easier to solve.
• It is knowledge-based: it relies on a symbolic description of the world, such as a set of rules.

However, the limits of traditional AI, which goal is to build systems that mimic human intelligence, are well-known: inevitably, a combinatorial explosion of the number of rules occurs due to the complexity of the environment. In fact, it is impossible to predict all the situations that will be encountered by an autonomous entity.

Situated or behavioral AI

In order to address these issues, another approach to decisional AI, also known as situated or behavioral AI, has been proposed. It does not attempt to model systems that produce deductive reasoning processes, but rather systems that behave realistically in their environment. The main characteristics of this approach are the following:

• It is bottom-up: it relies on elementary behaviors, which can be combined to implement more complex behaviors.
• It is behavior-based: it does not rely on a symbolic description of the environment, but rather on a model of the interactions of the entities with their environment.

The goal of situated AI is to model entities that are autonomous in their environment. This is achieved thanks to both the intrinsic robustness of the control architecture, and its adaptation capabilities to unforeseen situations.

The situated AI community has presented several solutions to the action selection mechanism. The first attempt to solve this problem goes back to subsumption architectures, which were in fact more an implementation technique than an algorithm. However, this attempt paved the way to several others, in particular the free-flow hierarchies and activation networks. A comparison of the structure and performances of these two mechanisms demonstrated the advantage of using free-flow hierarchies in solving the action selection problem. However, motor schemas and process description languages are two other approaches that have been used with success for autonomous robots.

Existing commercial technologies claiming the use of situated AI

• The DirectIA decision engine from MASA Group.

See also

Related Articles

• Artificial Intelligence
• Decision Tree
• Finite State Machine
• Expert System
• Automated Planning and Scheduling

External links

• Article artificial intelligence: The situated approach from the Encyclopædia Britannica