Numeric Domain Model Acquisition from Action Traces

规划技术的许多工业和商业应用都必须对数字进行推理。例如，在自主系统和机器人领域，自主机器人经常必须对其在空间中的位置、功率水平和存储容量进行推理。我们把机器人对其环境的内部表示称为它的世界模型。这些模型必须易于构建，理想情况下，它们应该是自动构建的。这个项目涉及到通过观察这些系统的运行来学习状态转换系统的形式化模型的主题。想想一个不熟悉国际象棋游戏的观察者：通过观察一系列的走法，可以学到很多关于游戏规则的知识。观察主教足够长的时间表明，这件作品只有对角移动是可能的。领域模型获取，或自动建模，是允许计算机通过观察动作来学习自己的世界模型的问题。我们将开发具有数字状态变量的状态转移系统的自动建模的新方法。当我们提到领域模型获取时，我们指的是从包括状态转移序列的示例数据中学习任何状态转移系统，无论是在自动规划、一般游戏玩、交互叙事、工作场所排班或任何其他类型的潜在问题的背景下。我们首先计划学习具有规划中常见的数字变量限制的领域模型；对行动成本的限制。这一限制意味着每个地面行动都有一个不变的成本，并且唯一的数字变量在整个计划的长度上累加这些单独的行动成本的总和。该累加值是优化变量。有动作成本的棋盘游戏是指在游戏的整个过程中积累分数的游戏。第一阶段的成功完成意味着我们有一个域模型获取算法来学习包括动作成本的模型。这类规划域是数值规划域的一个重要子集。然而，许多规划域包含更复杂的数值属性，尤其是任意数值变量和约束。因此，第二阶段将专注于开发学习这些约束的算法。该项目的完成将使人们能够简单地通过观察来学习许多不同问题的模型。例如，某些资源的容量限制、定位项目的维度限制以及在社交网络感知的交互叙事环境中实现某些动作的友谊级别要求。

Many industrial and commercial applications of planning technology have to reason about numbers. For example, in the area of autonomous systems and robotics, an autonomous robot often has to reason about its position in space, power levels and storage capacities. We call the internal representation that the robot has of its environment its model of the world. It is essential for these models to be easy to construct and ideally, they should be automatically constructed.This project concerns the subject of learning formal models of state-transition systems from observation of those systems operating. Consider an observer unfamiliar with the game of chess: by observing a sequence of moves, much can be learnt about the rules of the game. Watching a bishop for long enough demonstrates that only diagonal moves are possible for this piece.Domain model acquisition, or automated modelling, is the problem of allowing a computer to learn its own world model by observing actions. We will develop new methods of automated modelling for state transition systems with numeric state variables. And when we refer to domain model acquisition, we refer to the learning of any state transition system from example data that includes sequences of state transitions, whether that is in the context of automated planning, general game playing, interactive narrative, workplace rostering, or any other type of underlying problem.We first plan to learn models of domain with a common restriction of numeric variables in planning; the restriction to action costs. This restriction means that each ground action has a constant cost, and that the only numeric variable accumulates the sum of these individual action costs over the length of the plan. This accumulated value is the optimisation variable. Board games with action costs are those in which a score is accumulated throughout the play of the game.Successful completion of the first stage means that we have a domain model acquisition algorithm to learn models that include action costs. This class of planning domains is an important subset of numeric planning domains. However, many planning domains contain more complex numeric properties and, in particular, arbitrary numeric variables and constraints. The second stage, therefore, will concentrate on developing algorithms to learn these constraints. Completion of the project will allow models of many different problems to be learnt simply from observation. Examples include such things as capacity limits for certain resources, dimensional constraints for positioning items and strength of friendship level requirement to enable certain actions within a social-network aware interactive narrative setting.