Answer Set Solving in Practice

By Martin Gebser, Roland Kaminski, Benjamin Kaufmann, Torsten Schaub. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2012

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Inductive Logic Programming (2014, methodology): “In [19] grammars are learned using a special-purpose Prolog meta-interpreter.”
Model-free, Model-based, and General Intelligence (2018, background): “stochastic planning problems (FONDPs, PONDPs, MDPs, and POMDPs), general game playing, and answer set programming among others [Dechter, 2003; Biere et al., 2009; Pearl, 1988; Geffner and Bonet, 2013; Bertsekas, 1995; Kaelbling et al., 1998; Genesereth et al., 2005; Gebser et al., 2012].”
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco (2018, background): “Clingo leverages conflict reasoning [20] and heuristics [16] to direct the search process and efficiently solve problems with up to millions of variables.”
Multi-shot ASP solving with clingo (2017, methodology): “We illustrate this approach by adapting the Towers of Hanoi encoding from (Gebser et al. 2012) in Listing 5.”
Clingo = ASP + Control: Preliminary Report (2014, methodology): “We illustrate this by adapting the Towers of Hanoi encoding from (Gebser et al. 2012) Finally, the query is expressed in Line 18; its volatility is realized by making the actual goal condition goal_on(D,P), not on(D,P, t ) subject to the truth assignment to the external atom query( t ) .”