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remove U+0088 characters
LaTeX was complaining that this codepoint did not exist in the selected font. They turned out to indicate areas that had garbled list formatting, so fix the lists and remove the unneeded whitespace character. Fixes #7.
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docs/soar_manual/03_SyntaxOfSoarPrograms.md

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@@ -51,10 +51,13 @@ lowercase letters in a case-insensitive manner, for example, when using the
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[`print` command](../reference/cli/cmd_print.md). But internally, they are actually
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uppercase letters.)
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- **Constants**: There are three types of constants: integers, floating-point, and symbolic constants: - ˆInteger constants (numbers). The range of values depends on the machine
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- **Constants**: There are three types of constants: integers, floating-point, and symbolic constants:
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- Integer constants (numbers). The range of values depends on the machine
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and implementation you’re using, but it is at least [-2 billion...+2
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billion]. - ˆFloating-point constants (numbers). The range depends on the machine and
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implementation you’re using. - ˆSymbolic constants. These are symbols with arbitrary names. A constant can use
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billion].
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- Floating-point constants (numbers). The range depends on the machine and
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implementation you’re using.
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- Symbolic constants. These are symbols with arbitrary names. A constant can use
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any combination of letters, digits, or `$%&*+-/:<=>?_`. Other characters (such as blank
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spaces) can be included by surrounding the complete constant name with vertical
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bars: `|This is a constant|`. (The vertical bars aren’t part of the name; they’re

docs/soar_manual/04_ProceduralKnowledgeLearning.md

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@@ -112,15 +112,15 @@ gamut of tests that hand-written rules can and can have any element variablized.
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element.
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- For actions, the three elements refer to the identifier, attribute and
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value of the WME being created.
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- ˆAn element is either a variable, like `<s>` or a literal constant, like `23`,
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- An element is either a variable, like `<s>` or a literal constant, like `23`,
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`3.3`, or `someString`
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### Identity
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Before we can discuss the algorithm, we must first define one of its central
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concepts: *identity*.
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- ˆ**An identity is the set of all variables in a trace that refer to the same
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- **An identity is the set of all variables in a trace that refer to the same
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underlying object.**
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- So we can say that two *variables* are said to *share an identity* if they
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both refer to the same underlying object.
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non-operational conditions that it encounters while backtracing in the following
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manner:
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- ˆIt stores constraints on the value a single identity, for example>= 0,< 23.
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- ˆIt stores relational constraints between two identities, for example>
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- It stores constraints on the value a single identity, for example>= 0,< 23.
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- It stores relational constraints between two identities, for example>
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`<min>`, `< <max>` or `<> <other>`.
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- ˆEBC stores all of these constraints based on the underlying identities, not
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- EBC stores all of these constraints based on the underlying identities, not
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the variables used. For example, if a variable<foo>had the constraint<> <other>,
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EBC would record that the variables that share the identity of<foo>cannot have
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the same value as variables that share the identity of<other>.
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By default, explanation-based chunking is off.
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- ˆTo turn on chunking: `chunk always`
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- ˆTo turn off chunking: `chunk never`
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- To turn on chunking: `chunk always`
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- To turn off chunking: `chunk never`
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In real world agents, there may be certain problem spaces in which you don’t
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want your agent to learn rules. Chunking has a mechanism to allow agents to
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dynamically specify the states in which rules are learned.
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- ˆTo turn off chunking in all states except ones manually flagged on:
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- To turn off chunking in all states except ones manually flagged on:
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- Use chunk only setting.
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- Design an agent rule that executes the RHS action force-learn, which only
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matches in states in which you want to learn rules.
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-ˆTo turn on chunking in all states except ones manually flagged off:
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- To turn on chunking in all states except ones manually flagged off:
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- Use chunk except setting.
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- Design an agent rule that executes the RHS action dont-learn, which only
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matches in states in which you don’t want to learn rules.
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**Printing Rules**
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ˆTo print all chunks learned:
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- To print all chunks learned:
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`print --chunks` or `print -c`
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ˆTo print all justifications learned (and still matching):
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- To print all justifications learned (and still matching):
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`print --justifications` or `print -j`
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ˆTo print a rule or justification:
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- To print a rule or justification:
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`print <rule-name>`
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For more information on print, see section 9.3.1 on page 217.
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**Trace Messages**
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ˆTo print when new rules are learned (just the name):
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- To print when new rules are learned (just the name):
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`trace --learning 1` or `trace -l 1`
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ˆTo print when new rules are learned (the full rule):
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- To print when new rules are learned (the full rule):
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`trace --learning 2` or `trace -l 2`
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ˆTo print a trace of the conditions as they are collected during backtracing:
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- To print a trace of the conditions as they are collected during backtracing:
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`trace --backtracing` or `trace -b`
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ˆTo print warnings about chunking issues detected while learning:
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- To print warnings about chunking issues detected while learning:
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`trace --chunk-warnings` or `trace -C`
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ˆTo print when learned chunks match and fire:
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- To print when learned chunks match and fire:
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`trace --backtracing` or `trace -b`
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For more information on trace, see section 9.6.1 on page 259.
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#### Interrupting Execution To Examine Learning
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ˆTo stop Soar after each successful learning episode:
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- To stop Soar after each successful learning episode:
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```
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chunk interrupt on
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```
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ˆTo stop Soar after detecting any learning issue:
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- To stop Soar after detecting any learning issue:
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```
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chunk warning-interrupt on
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```
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ˆTo stop Soar after learning a rule that the explainer recorded:
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- To stop Soar after learning a rule that the explainer recorded:
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```
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chunk explain-interrupt on
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```
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knowledge, for example, operator selection knowledge.
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- <a name="footnote2">[2]</a>: Justifications can have variables in the negated
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conditions and negated conjunctions of conditions. They just don’t have any
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variables in its positive conditions.
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variables in its positive conditions.

docs/soar_manual/05_ReinforcementLearning.md

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@@ -301,10 +301,12 @@ operator- no-change impasse. When this occurs, the number of decision cycles the
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RL operator at the superstate remains selected is dependent upon the processing
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in the impasse state. Consider the operator trace in Figure 5.1.
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ˆAt decision cycle 1, RL operatorO1is selected inS1and causes an
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operator-no-change impass for three decision cycles. ˆIn the substateS2,
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operatorsO2,O3, andO4are selected and applied sequentially. ˆMeanwhile inS1,
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rewardsr 2 ,r 3 , andr 4 are put on thereward-linksequentially. ˆFinally, the
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- At decision cycle 1, RL operatorO1is selected inS1and causes an
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operator-no-change impass for three decision cycles.
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- In the substateS2,
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operatorsO2,O3, andO4are selected and applied sequentially.
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- Meanwhile inS1,
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rewardsr 2 ,r 3 , andr 4 are put on thereward-linksequentially. - Finally, the
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impasse is resolved by O4, the proposal for O1 is retracted, and RL operatorO5is
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selected inS1.
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**numeric-indifferent-mode** is set to
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**sum**. In general, the RL mechanism only works correctly when this is the
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case, and we assume this case in the rest of the chapter. See page 198 for more
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information about this parameter.
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information about this parameter.

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