idp_engine.Problem¶

Class to represent a collection of theory and structure blocks.

class idp_engine.Problem.Problem(*blocks, extended=False)[source]¶

A collection of theory and structure blocks.

extended¶

True when the truth value of inequalities and quantified formula is of interest (e.g. in the Interactive Consultant)

Type: Bool

declarations¶

the list of type and symbol declarations

Type: dict[str, Type]

constraints¶

a set of assertions.

Type: OrderedSet

assignments¶

the set of assignments. The assignments are updated by the different steps of the problem resolution. Assignments include inequalities and quantified formula when the problem is extended

Type: Assignment

clark¶

A mapping of defined symbol to the rule that defines it.

Type: dict[(SymbolDeclaration, Definition), Rule]

def_constraints¶

A mapping of defined symbol to the whole-domain constraint equivalent to its definition.

Type: dict[SymbolDeclaration, Definition], Expression

interpretations¶

A mapping of enumerated symbols to their interpretation.

Type: dict[string, SymbolInterpretation]

goals¶

A set of goal symbols

Type: dict[string, SymbolDeclaration]

_formula¶

the logic formula that represents the problem.

Type: Expression, optional

co_constraints¶

the set of co_constraints in the problem.

Type: OrderedSet

classmethod make(theories, structures, extended=False)[source]¶: polymorphic creation

assert_(code: str, value: Any, status: idp_engine.Assignments.Status = <Status.GIVEN: 2>)[source]¶

asserts that an expression has a value

Parameters

code (str) – the code of the expression, e.g., “p()”
value (Any) – a Python value, e.g., “True”
status (Status, Optional) – how the value was obtained. Default: Status.GIVEN

formula()[source]¶: the formula encoding the knowledge base

expand(max=10, complete=False)[source]¶: output: a list of Assignments, ending with a string

symbolic_propagate(tag=<Status.UNIVERSAL: 4>)[source]¶: determine the immediate consequences of the constraints

propagate(tag=<Status.CONSEQUENCE: 6>)[source]¶: determine all the consequences of the constraints

get_range(term: str)[source]¶: Returns a copy of the problem, with its assignments property containing a description of the possible values of the term.

explain(consequence)[source]¶

returns the facts and laws that justify ‘consequence in the ‘self Problem

Parameters

self (Problem) – the problem state
consequence (string) – the code of the sentence to be explained. Must be a key in self.assignments

Returns

list of facts and laws that explain the consequence

Return type

(facts, laws) (List[Assignment], List[Expression])]

simplify()[source]¶

returns a simpler copy of the Problem, using known assignments

Assignments obtained by propagation become fixed constraints.

decision_table(goal_string='', timeout=20, max_rows=50, first_hit=True, verify=False)[source]¶

returns a decision table for goal_string, given self.

Parameters

goal_string (str, optional) – the last column of the table.
timeout (int, optional) – maximum duration in seconds. Defaults to 20.
max_rows (int, optional) – maximum number of rows. Defaults to 50.
first_hit (bool, optional) – requested hit-policy. Defaults to True.
verify (bool, optional) – request verification of table completeness. Defaults to False

Returns

the non-empty cells of the decision table

Return type

list(list(Assignment))