Behavior-Based Methods for Modeling and Structuring in .NET Print Code 128 in .NET Behavior-Based Methods for Modeling and Structuring

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Behavior-Based Methods for Modeling and Structuring generate, create barcode 128 none with .net projects rfid representat Code-128 for .NET ion into behavior-based systems (Matari , 1992); the represenc tation is inherently distributed. Behavior-based systems are best suited for environments with signi cant dynamic changes, where fast response and adaptivity are necessary, but the ability to do some looking ahead and avoid past mistakes is also useful (Matari , 2002b).

c System decomposition by activity (Brooks, 1991) ensures a concrete connection between perception and action, a principle already described above, in the context of primitives. Behaviors represent activities not because of the semantics of their labels, as in classical AI, but because they capture inherent regularity of the interaction dynamics of the robot and the world. Behaviors are thus encapsulations of dynamics, and are made general through parametrization.

The repercussions of the new behavioral organization continue to have an impact on robotics and AI. What had essentially been suggested was that the direction that had been taken since the eld s inception was incorrect and founded on the idea that would not carry over to physical robots. In many cases the traditional behavioral organization was assumed at the time to be the only way to structure systems.

Many had assumed that scaling issues (e.g., to large or continuous state-spaces), issues of partialobservability, non-stationarity, and uncertainty, could (and further should) be addressed from within their traditional representation.

3.2 Representational Issues The pioneering role of the Subsumption Architecture, the title of Brooks (1991), and an unfortunate lexical collision with behaviorism in psychology have all resulted in the broadly accepted misconception that behaviorbased systems do not permit representation. As early as Matari (1992), c representation was introduced into behavior-based systems, in that case in the context of topological spatial mapping and path- nding.

Matari (1992, 1997a) describes the work with Toto, a mobile robot that c was rst to use dynamic behaviors, created and activated whenever needed to represent landmarks in the environment. Planning, previously absent in behavior-based systems, is achieved in Toto through spreading activation within the network of map behaviors. Matari (1992) describes high-level c competencies for landmark detection using unique time-extended sensory signatures.

The landmark behaviors are used to ll behavioral-slots, resulting in a graph of active map locations. This coupling of action, perception, and representation is similar to the mirror-neuron and motor primitive model already described, but Toto s most signi cant behaviors are several cognitive degrees of separation from basic motor control. Matari c (1992) mentions that this mechanism falls under the broad umbrella of ideas termed cognitive maps, and that it is representative of a particular interpretation of the organization and function of the rat hippocampus.

The faithfulness of this type of representation to the actions that the robot. Dylan A. Shell and Maja J. Matari c could perfo Visual Studio .NET barcode 128 rm and the constraints and dynamics that structure its action space are summed up in the maxim: behavior-based systems think the way they act. (Matari , 2002b).

c Decety (1996) and Jeannerod and Decety (1995) provide evidence indicating that biological systems may operate in the same manner, showing that both imagined and executed movements share the same neural substrate. When simply imagining or visualizing a movement, subjects motor pathways exhibited activation similar to that which occurs during actually performing the movement. This evidence points to principles employed by behavior-based architectures from a organization level, as well as to their embodied approach to representation.

The traditional view holds that higher-level cognitive capabilities are best modeled symbolically. As an alternative, Nicolescu and Matari (2002) c describe a hierarchical behavior-based architecture that enables behaviors to represent more abstract concepts. The inclusion of both external and sequential preconditions allows their abstract behaviors, to cope with temporal sequencing whereas maintaining the conventional concurrent execution.

Their network abstract behavior hides the details of an entire network of behaviors, and presents an external interface as if it were a single behavior; recursive application enables general hierarchical representations. The work thus allows for representing temporal sequences and hierarchical structures, without using plan operators or symbolic mechanisms. As a result, there is no need to produce a middle layer to bridge the difference-inkind between reactive and symbolic layers in hybrid systems, the common alternative to behavior-based systems.

Nicolescu (2002, pg. 68) describes expressive power of the framework in terms of a particular human robot interaction task. Here, and in the Toto work, and generally in behavior-based systems, representations are stored in a distributed fashion, so as to best match the underlying modularity that produces the robot s observable behavior.

If a robot needs to make high-level decisions (e.g., plan ahead), it does so in a network of communicating behaviors, rather than a single components (e.

g., a centralized planner). Thus, the very same characteristics that structure the robot s action space also have an impact on the way the robot represents and reasons about the world, as in biological evidence indicated above.

3.3 Behavior Composition The previous sections have, for the most part, discussed only single behaviors; important questions arise when collections of behaviors are considered. Typically, behaviors are hand-designed to perform a particular activity, attain a goal, or maintain some state.

It is, of course, impossible to de ne an optimal behavior set. Nevertheless, practical experience has established a number of consistent guiding principles..

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