Higher-level reasoning in Software Integrated Code 128 Code Set A in Software Higher-level reasoning

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11.3 Higher-level reasoning using barcode integrating for software control to generate, create ansi/aim code 128 image in software applications. gs1 bar code 128 User networks Agent language Decisions on what and to whom to communicate Logical representation Internal representation. Agent language Code 128 Code Set C for None Decisions on what and to whom to communicate Logical representation Internal representation. Agent language Decisions on what and to whom to communicate Logical representation Internal representation. External world Figure 11.10 Layered representation of knowledge in a sensor network. Language Stati Code 128A for None stical tools and formal logic, while important, are of course not all that there is to reasoning. Statistics-based systems are limited to the categories innate in their initial design. What is required is formal reasoning on top of a hypothesis space, so that new events can be discussed.

Language (an agreement on meanings and a structured system of expression) also has an important role to play. Words can be very highly compressed representations of reality compared with raw sensor data. In exchange for language conveying a little less knowledge of what has been seen, it provides the ability to reason logically about things that have not been seen before.

For example, having formed agreement on the concept of a cat, one can then inductively reason about likely behaviors for the class based on behaviors seen in particular cats or similar animals. This can expand the set of hypotheses that can then be tested by the sensor network that observes the feline population. At the same time, reasoning purely at the high level is not sufficient.

Fusion and other forms of decision-making should take place at multiple levels, with appropriate training and adaptation according to the level of abstraction. Consider, e.g.

, the system depicted in Figure 11.10. The internal representation is concerned with raw data and results of signal processing, such as from transforms or coherent combining.

Only certain aspects of the external world are represented, as driven by the capabilities of the sensors and the priorities expressed by some set of applications. Generally what occurs is very application-specific. The information represented may be quite different from what humans perceive, e.

g., widely divergent views of the same scene, magnetic readings, etc. Not everything in.

Data management the internal l Code-128 for None evel is represented with significant fidelity at the logical level upon which reasoning takes place. Decisions to be made include whether to communicate with neighbors, and if so what to communicate. This decision-making employs a language (at the level of the agents), whose design has to trade the need for a compact representation (to minimize communication resources) against that to be sufficiently expressive to extend to new phenomena.

The agent language must also have an interface (mapping) to human languages to process queries that proceed to the internal level, and to present results in a form that can be unambiguously understood. In the development of such a language there are two fundamental problems. The frame problem relates to the scope of the event set to be described.

This must be limited, or else the computational time is large and it is difficult to create a concise structure. The solution is first to filter at the lower levels of representation so that only interesting events are presented for symbolic representation. Interest is defined through some combination of innate (designed) abilities, human-assisted learning, and deduction (from manipulation of the evolving language).

The language framework itself may be hierarchical, with lower layers dealing with particular sets of events (e.g., fusion), depending on who is involved, and higher layers performing inference of motives or other highly abstracted tasks.

In this way structures and vocabulary can be optimized to particular applications. The second major issue is known as grounding: i.e.

, how to bind experience to linguistically relevant variables that can be communicated with others. Where one sensor might designate an event by sporg another might use zruk; somehow they must agree that the event was indeed in common and assign the same descriptor to it. This problem has long been known to be insoluble in the abstract, but yet obviously humans solve it in the particular.

The key to success is use of in-born constraints on system attention together with a basic language structure (e.g., a basic grammar) shared by all nodes.

This might include a bias towards discussing particular events (e.g., motion accompanied by sound), increasing the likelihood that nodes will discuss something in common.

(For example, one might employ an adaptive belief network for some of the subtasks.) It has been proven that a broad class of expressive languages, including some non-regular context-free languages, are learnable with such constraints. Further, learnability is considerably improved if the system recovers semantic relations among the symbols in addition to symbol sequences, e.

g., by knowing approximately what another sensor is talking about. Training procedures and some basic innate structure can thus play an important role in enabling languages which can evolve in a sensor network setting.

Such capabilities have been demonstrated in systems using the capabilities of Prolog. Notice that in this system there remain a number of roles for human users: design of the innate capabilities, establishment of priorities, and assistance in training. It is na ve to expect that an automated reasoning system will achieve our full set of patternrecognition properties in the near future.

However, the system will in other ways extend well beyond the capabilities of human observers: persistence of observation, capability for sorting through large numbers of observations to find the interesting events, and sensing modes beyond the capabilities of humans. Larger-scale systems must achieve greater degrees of autonomy, or costs of maintenance and deployment will become prohibitive. Thus, over time intelligence will be pushed more deeply into.

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