Answer set programming and plan generation

By Vladimir Lifschitz. Artificial Intelligence, 2002

518 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3896d833f428bb483e15de1a584da4f2348797e6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Conflict-driven answer set solving: From theory to practice (2012, background): “Answer Set Programming (ASP; [67,94,102,66,87,6,65]) has become an attractive paradigm for knowledge representation and reasoning, due to its appealing combination of rich yet simple modeling languages1 with powerful solving engines.”
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, methodology): “Following best practices in logic program design [34], we define the space of possible visualization specifications with two sets of rules: (1) a set of aggregate rules that specifies the domains of the attributes defined in the previous section (mark, encoding, .”
Answer Set Solving in Practice (2012, unknown): “Books [4], [29], [53] Surveys [50], [2], [39], [21], [11] Articles [41], [42], [6], [61], [54], [49], [40], etc.”
Neural-Symbolic Learning and Reasoning: Contributions and Challenges (2015, methodology): “…so-called propositionalization methods, as used by ILP (Blockeel et. al, 2011; França, Zaverucha, and d'Avila Garcez, 2014) and answer-set programming (Lifschitz, 2002), and modal logics (d'Avila Garcez, Lamb and, Gabbay, 2007), known to be more expressive than propositional logic and decidable.”
Potassco: The Potsdam Answer Set Solving Collection (2011, unknown): “The ﬁrst two rules generate possible solution candidates.”
Nonmonotonic causal theories (2004, background): “Since causal theories are closely related to logic programs, it can be also viewed as a form of answer set programming [23,32] applied to plan generation [6,20].”