Domain-specific heuristics are an essential technique for solving
combinatorial problems efficiently. Current approaches to integrate
domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory
when dealing with heuristics that are specified non-monotonically on the basis
of partial assignments. Such heuristics frequently occur in practice, for
example, when picking an item that has not yet been placed in bin packing.
Therefore, we present novel syntax and semantics for declarative specifications
of domain-specific heuristics in ASP. Our approach supports heuristic
statements that depend on the partial assignment maintained during solving,
which has not been possible before. We provide an implementation in ALPHA that
makes ALPHA the first lazy-grounding ASP system to support declaratively
specified domain-specific heuristics. Two practical example domains are used to
demonstrate the benefits of our proposal. Additionally, we use our approach to
implement informed} search with A*, which is tackled within ASP for the first
time. A* is applied to two further search problems. The experiments confirm
that combining lazy-grounding ASP solving and our novel heuristics can be vital
for solving industrial-size problems.

本研究提出一种新的语法和语义，声明性地说明 ASP 中具有领域特定的启发式方法，这些启发式方法直接依赖于维护的部分分配，实现了懒惰打地基即 ASP 的第一个系统支持领域特定的声明性启发式，通过在两个实际示例领域上应用我们的方法，以及使用我们的方法来实现 A * 搜索，该搜索首次在 ASP 中处理，并用于进一步的两个搜索问题。实验结果表明，组合懒惰打地基 ASP 求解和我们的启发式方法对于解决工业规模问题至关重要。

在答案集规划中指定和利用非单调领域特定的声明性启发式

Specifying and Exploiting Non-Monotonic Domain-Specific Declarative Heuristics in Answer Set Programming

AlphaZero and its extension MuZero are computer programs that use
machine-learning techniques to play at a superhuman level in chess, go, and a
few other games. They achieved this level of play solely with reinforcement
learning from self-play, without any domain knowledge except the game rules. It
is a natural idea to adapt the methods and techniques used in AlphaZero for
solving search problems such as the Boolean satisfiability problem (in its
search version). Given a search problem, how to represent it for an
AlphaZero-inspired solver? What are the "rules of solving" for this search
problem? We describe possible representations in terms of easy-instance solvers
and self-reductions, and we give examples of such representations for the
satisfiability problem. We also describe a version of Monte Carlo tree search
adapted for search problems.

AlphaZero 和 MuZero 是使用机器学习技术进行超级人类棋类游戏表现的程序，并尝试将其应用于解决搜索问题，本文描述了在搜索问题中如何使用 AlphaZero 技术进行表示以及求解规则，并提出了适用于该类问题的一种 Monte Carlo tree search 方法。