Implementation Of A Very Large Knowledge-Based Domain- Specific Heuristic Problem Solving Systems

Synchem系统是一个大型的基于知识的特定领域 启发式问题解决程序，能够找到有效的 重要有机分子的合成路线 和复杂性，而不需要用户的在线指导。 Synchem需要广泛的知识基础，使其成为常规 有用的. 然而，很难让领域专家参与进来， 长期的奉献精神和必要的承诺强度 创造一个真正有生产力的知识基础。 为了 处理削弱知识库的瓶颈， 将设计出可以使用大型计算机的学习程序， 特定反应实例的可读数据库，以提供 用于算法的训练示例， 通过归纳和演绎， 一般化 这种方法将得到加强， 通常被描述为基于简化的方法 学习 由于大多数情况下的个人反应条目 数据库通常是随机排序和分类的，另一个 机器学习程序将输入数据库划分为 连贯的反应类使用的方法，概念 聚类 为了使Synchem能够处理 合理途径的爆炸，这将是一个 成功的努力，大大扩大了知识基础， 推理机将重新制定为分布式并行 探索问题空间。

The Synchem systems is a large knowledge-based domain specific heuristic problem solving program that is able to find valid synthesis routes for organic molecules of substantial interest and complexity without online guidance on the part of its user. Synchem requires an extensive knowledge base to make it routinely useful. However, it is very difficult to engage domain experts to the long-term dedication and intensity of commitment necessary to create a genuinely productive knowledge base. In order to deal with the debilitating knowledge-base bottleneck, machine learning programs will be devised that can use large computer- readable databases of specific reaction instances to provide training examples for algorithms designed to extract the underlying reaction schemata through inductive and deductive generalization. This approach will be augmented by the methodology that is usually described as explanation-based learning. Since the individual reaction entries in most databases are often haphazardly sorted and classified, another machine learning program will partition the input databases into coherent reaction classes using the methodology of conceptual clustering. To make it possible for Synchem to deal with the explosion of reasonable pathways that would be the result of a successful effort to greatly expand the knowledge base, the inference engine will reformulated for distributed parallel search of the problem space.