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1. SMT-based Constraint Answer Set Solver EZSMT+ for Non-tight Programs

   Shen, D. (January 2018, 16th International Conference on Principles of Knowledge Representation and Reasoning (KR))
2. Answer Set Planning: A Survey

   https://doi.org/10.1017/s1471068422000072  

   CAO TRAN, SON; PONTELLI, ENRICO; BALDUCCINI, MARCELLO; SCHAUB, TORSTEN (April 2022, Theory and Practice of Logic Programming)

   Abstract Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans , that is, solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research. 

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3. Explanations for Answer Set Programming

   Mario Alviano, Ly Ly (January 2023, International Conference on Logic Programming)
4. Axiomatization of Aggregates in Answer Set Programming

   https://doi.org/10.1609/aaai.v36i5.20504  

   Fandinno, Jorge; Hansen, Zachary; Lierler, Yuliya (June 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   The paper presents a characterization of logic programs with aggregates based on many-sorted generalization of operator SM that refers neither to grounding nor to fixpoints. This characterization introduces new symbols for aggregate operations and aggregate elements, whose meaning is fixed by adding appropriate axioms to the result of the SM transformation. We prove that for programs without positive recursion through aggregates our semantics coincides with the semantics of the answer set solver Clingo. 

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5. Explainable Planning Using Answer Set Programming

   https://doi.org/10.24963/kr.2020/66  

   Nguyen, Van; Vasileiou, Stylianos Loukas; Son, Tran Cao; Yeoh, William (July 2020, Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning)

   null (Ed.)

   In human-aware planning problems, the planning agent may need to explain its plan to a human user, especially when the plan appears infeasible or suboptimal for the user. A popular approach to do so is called model reconciliation, where the planning agent tries to reconcile the differences between its model and the model of the user such that its plan is also feasible and optimal to the user. This problem can be viewed as an optimization problem, where the goal is to find a subset-minimal explanation that one can use to modify the model of the user such that the plan of the agent is also feasible and optimal to the user. This paper presents an algorithm for solving such problems using answer set programming. 

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