lpconvert
grounding tools for (disjunctive) logic programs
Install
- All systems
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curl cmd.cat/lpconvert.sh
- Debian
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apt-get install gringo - Ubuntu
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apt-get install gringo -
Alpine
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apk add gringo - Arch Linux
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pacman -S clingo - Kali Linux
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apt-get install gringo - Windows (WSL2)
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sudo apt-get updatesudo apt-get install gringo - OS X
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brew install clingo - Raspbian
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apt-get install gringo - Dockerfile
- dockerfile.run/lpconvert
- Docker
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docker run cmd.cat/lpconvert lpconvertpowered by Commando
gringo
grounding tools for (disjunctive) logic programs
Current answer set solvers work on variable-free programs. Hence, a grounder is needed that, given an input program with first-order variables, computes an equivalent ground (variable-free) program. This package contains the following tools: - gringo: creates lparse-compatible variable-free programs. - clingo: stands for clasp on gringo and combines both systems in a monolithic way. Its input language is that of gringo and its output corresponds to that of clasp. - iclingo: an incremental answer set programming system, which is based on the idea that the grounder as well as the solver are implemented in a stateful way. Thus, both keep their previous states while increasing an incremental parameter. As regards grounding, at each incremental step, the goal is to produce only ground rules stemming from the current program slice, without re-producing previous ground rules. The ground program slices are then gradually passed to the solver that accumulates ground rules and computes answer sets for them. - oclingo: a system for reactive answer set programming, extending gringo and clasp for handling external modules provided at runtime by a controller.
clasp
conflict-driven nogood learning answer set solver
clasp is an answer set solver for (extended) normal logic programs. It combines the high-level modeling capacities of answer set programming (ASP) with state-of-the-art techniques from the area of Boolean constraint solving. The primary clasp algorithm relies on conflict-driven nogood learning, a technique that proved very successful for satisfiability checking (SAT). Unlike other learning ASP solvers, clasp does not rely on legacy software, such as a SAT solver or any other existing ASP solver. Rather, clasp has been genuinely developed for answer set solving based on conflict-driven nogood learning. clasp can be applied as an ASP solver (on LPARSE output format), as a SAT solver (on simplified DIMACS/CNF format), or as a PB solver (on OPB format).