Decision Support Systems in Agriculture: Some Successes and a Bright Future - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 297

fails to find the correct answer, we tend to conclude that person is a novice! Experts
frequently use the Best-first, where they may switch between search strategies based on
their experience and awareness of the decision-tree content.

Depth 1

1, 2, 3

Depth 2

1,2,3 2

Depth 3

1 3

Fig. 1. Decision-tree illustrating Depth-first searches (pathway example 1), Breadth-first
(follow pathway 2), and Best-first (follow pathway 3).

Recently, there has been renewed interest in search strategies that can exploit the rapidly
expanding information base on the Internet (Watson-Jeopardy, 2011). These strategies may
make qualitative information much more accessible to computer based reasoning systems.

5.4 Reasoning methods
A third contribution of AI to agricultural decision-aids (the first being Knowledge
representation , the second is Search Strategies ) is the choice between forward-chaining and
backward chaining in terms of flow of the reasoning or inference through the decision-tree
or set of rules. The forward-chaining method of reasoning begins with the observed facts
and makes all possible inferences on the first pass through the decision-tree. The second and
subsequent passes collect all facts originally observed plus all conclusions resulting from the
first pass through the decision-tree. When the entire decision-tree is evaluated and all
possible inferences are made the process is complete. The backward-chaining method begins
with the same decision-tree but first evaluates the “goals” or final conclusions or inferences
of the decision-tree, of which there typically only a few. Each of these “goals” is evaluated
one at a time by determining what facts are needed for each of the goals to be concluded
(succeed in being inferred). If any facts are missing that are needed for a specific goal, then
that goal is discarded and the next unevaluated goal is similarly evaluated. Many of the
initial expert system software programs chose backward-chaining as a reasoning strategy.
The backward-chaining method of reasoning or progress through the decision-tree is often
much more rapid than forward-chaining because major portions of the decision-tree are
truncated if any rule does not have all of the necessary information and thus is evaluated as
false. Readers interested in further details of these reasoning strategies are encouraged to
consult recent texts or summaries on AI.
As this chapter is being written, new techniques of reasoning are illustrating that machines
such as IBM's Watson can account for uncertainty in information and situations, by rank
ordering multiple solutions to a given problem. The result is better performance than the
best human players of the game Jeopardy (Watson-Jeopardy, 2011). This event is sure to be a