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In the elephant paper [Brooks 1990] appearing in the proceedings of the predecessor of the current workshop, Brooks criticizes the ungroundedness of traditional symbolic AI systems, and proposes physically grounded systems as an alternative, particularly the subsumption architecture. Subsumption has been highly successful in generating a variety of interesting and seemingly intelligent behaviors in a variety of mobile robots. As such it has established itself as an influential approach to generating complex physical behavior in autonomous agents. In the current paper we explore the possibilities for integrating the old with the new, in an autonomous agent architecture that ranges from physical behavior generation inspired by subsumption to classical knowledge representation and reasoning, and a new proposed level in between the two. Although we are still struggling with many of the issues involved, we believe we can contribute to a solution for some of the problems for both classical systems and physically grounded systems mentioned in [Brooks 1990], in particular:
By an architecture we mean an organization of components of a system,
what is integral to the system, and how the various components
interact. Which
components go into an architecture for an autonomous agent has
traditionally depended to a large extent on whether we are building a
physical system, understanding/modeling behaviors of an
anthropomorphic agent, or integrating a select number of behaviors.
The organization of an architecture may also be influenced by whether or
not one adopts the modularity assumption of Fodor [Fodor 1983], or
a connectionist point of view, e.g. [McClelland et al. 1986], or an
anti-modularity assumption as in Brooks's subsumption architecture
[Brooks 1985]. The modularity assumption supports (among other
things) a division of the mind into a central system, i.e., cognitive
processes such as learning, planning, and reasoning, and a peripheral
system, i.e., sensory and motor processing [Chapman 1990]. Our
architecture is characterized by a three-level organization into a
Knowledge level (KL), a Perceptuo-Motor level (PML), and a Sensory-Actuator
level (SAL). This organization is neither modular, anti-modular,
hierarchical, anti-hierarchical, nor connectionist in the conventional
sense. It integrates a traditional symbol system with a physically grounded
system, i.e., a behavior-based architecture. The most important
difference with a behavior-based architecture like Brooks's subsumption is
the presence of three distinct levels with different representations and
implementation mechanisms for each, particularly the presence of an
explicit Knowledge level. Representation, reasoning (including planning),
perception, and generation of behavior are distributed through all three
levels. Our architecture is best described using a resolution pyramid
metaphor as used in computer vision work [Ballard \& Brown 1982], rather than a
central vs. peripheral metaphor.
Architectures for building physical systems, e.g., robotic architectures
[Albus et al. 1981], tend to address the relationship between a physical entity,
(e.g., a robot), sensors, effectors, and tasks to be accomplished. Since
these physical systems are performance centered, they often lack general
knowledge representation and reasoning techniques. These architectures
tend to be primarily concerned with the body, that is, how to get the
physical system to exhibit intelligent behavior through its physical
activity. We say these systems are not concerned with consciousness.
These architectures address what John Pollock calls Quick and
Inflexible (Q&I) processes [Pollock 1989].
We define consciousness for a robotic agent operationally as being aware of
one's environment, as evidenced by (1) having some internal states or
representations that are causally connected to the environment through
perception, (2) being able to reason explicitly about the environment, and
(3) being able to communicate with an external agent about the
environment.
Architectures for understanding/modeling behaviors of an anthropomorphic agent, e.g., cognitive architectures [Langley et al. 1991][Pollock 1989][Anderson 1983], tend to address the relationships that exist among the structure of memory, reasoning abilities, intelligent behavior, and mental states and experiences. These architectures often do not take the body into account. Instead they primarily focus on the mind and consciousness. Our architecture ranges from general knowledge representation and reasoning to body-dependent physical behavior, and the other way around.
We are interested in autonomous agents that are
embedded in
a dynamic environment. Such an agent needs to continually interact with and
react to its environment and exhibit intelligent behavior through its
physical activity. To be successful, the agent needs to reason about events
and actions in the abstract as well as in concrete terms. This means
combining situated activity with acts based on reasoning about
goal-accomplishment, i.e., deliberative acting or planning. In the latter
part of this paper, we will present a family of agents based on our
architecture. These agents are designed with a robot in mind, but their
structure is also akin to anthropomorphic agents.
Figure
schematically presents our architecture.
There are several features that contribute to the robustness of our architecture. We highlight them below (an in-depth discussion follows later):