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        "description": "# PyExpert 0.0.1\n\nThis is a small embeddable Prolog interpreter in Python3, designed primarily for\nimplementing explainable expert systems.\n\n\n\nUsage:\n\n```python\n\n# Imports\nfrom weak.prolog import prolog_driver,prolog_default_env,prolog_next_solution,prolog_core_library\n\n# Initialise environment\nenv = prolog_default_env()\n\n# Execute query\nret,vars = prolog_driver(env, '? append(A, B, [x,y]).')\nprint(vars)\n\n# Inspect all the remaining solutions\nwhile prolog_next_solution(env):\n    print(vars)\n\n```\n\n# Weak sets\n\nThe main feature of this implementation is a `weak set` - a value that unifies\nwith any other weak set, resulting in a new set containing elements of both\nsets. This is useful for implementing certain kinds of type systems and for\nhigher level hacks, such as tracing a Prolog execution (e.g., for a\nhuman-readable narration of an expert system decision), implementing\nconstraints, etc.\n\nFor example:\n\n```prolog\n? weak(a,W1), weak(b,W1), weak(c,W2), W1=W2.\n```\n\nresults in `W1=W2=[a,b,c]`\n\nSee `weak/narrate.py` for an example of an instrumentation of a Prolog code for\nproducing execution traces, or a \"narration\", which can then be used to\ngenerate, for example, a plain English narration of the expert system thought\nprocess.\n\nSee `tests/demo.py` for an example of constructing an explainable expert system.\n\n\n",
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