In artificial intelligence research, the situated approach builds agents that are designed to behave effectively successfully in their environment. This requires designing AI \"from the bottom-up\" by focussing on the basic perceptual and motor skills required to survive. The situated approach gives a much lower priority to abstract reasoning or problem-solving skills. <\/p>\n
The approach was originally proposed as an alternative to traditional approaches (that is, approaches popular before 1985 or so. After several decades of success, these older approaches to modeling decision-making, such as expert systems, finite state machines or decision trees reached their limitations in the 1980s when researchers tried to use them to drive real robots in uncertain environments. In fact, classical AI technologies face intractable issues, such as combinatorial explosion, when confronted with real-world modeling problems, and several directions have been explored by researchers to address these issues. All these approaches focus on modeling intelligence situated in a given environment: they have come to be known as the situated approach to AI. <\/p>\n
During the late 1980s, the approach now known as nouvelle AI (nouvelle means new in French) was pioneered at the MIT Artificial Intelligence Laboratory by Rodney Brooks. As opposed to classical or traditional artificial intelligence, nouvelle AI purposely avoided the traditional goal of modeling human-level performance, but rather tries to create systems with intelligence at the level of insects, closer to real-world robots. But eventually, at least at MIT new AI did lead to an attempt for humanoid AI in the Cog Project. <\/p>\n
The conceptual shift introduced by nouvelle AI flourished in the robotics area, given way to behavior-based artificial intelligence (BBAI), a methodology for developing AI based on a modular decomposition of intelligence. It was made famous by Rodney Brooks: his subsumption architecture was one of the earliest attempts to describe a mechanism for developing BBAI. It is extremely popular in robotics and to a lesser extent to implement intelligent virtual agents because it allows the successful creation of real-time dynamic systems that can run in complex environments. For example, it underlies the intelligence of the Sony, Aibo and many RoboCup robot teams. <\/p>\n
Realizing that in fact all these approaches were aiming at building not an abstract intelligence, but rather an intelligence situated in a given environment, they have come to be known as the situated approach. In fact, this approach stems out from early insights of Alan Turing, describing the need to build machines equipped with sense organs to learn directly from the real-world instead of focusing on abstract activities, such as playing chess. <\/p>\n
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. <\/p>\n
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 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 are actions 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?). <\/p>\n
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: <\/p>\n
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. <\/p>\n
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: <\/p>\n
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