Regardless of the particular task we want them to perform in an environment,
there are often shared safety constraints we want our agents to respect. For
example, regardless of whether it is making a sandwich or clearing the table, a
kitchen robot should not break a plate. Manually specifying such a constraint
can be both time-consuming and error-prone. We show how to learn constraints
from expert demonstrations of safe task completion by extending inverse
reinforcement learning (IRL) techniques to the space of constraints.
Intuitively, we learn constraints that forbid highly rewarding behavior that
the expert could have taken but chose not to. Unfortunately, the constraint
learning problem is rather ill-posed and typically leads to overly conservative
constraints that forbid all behavior that the expert did not take. We counter
this by leveraging diverse demonstrations that naturally occur in multi-task
settings to learn a tighter set of constraints. We validate our method with
simulation experiments on high-dimensional continuous control tasks.

从专家演示的安全任务完成中学习约束条件的方法扩展了逆强化学习（IRL）技术到约束空间，通过利用多任务环境中自然发生的各种演示来学习更紧密的约束条件。

从多任务演示中学习共享安全约束

Learning Shared Safety Constraints from Multi-task Demonstrations

Efficient decision-making over continuously changing data is essential for
many application domains such as cyber-physical systems, industry
digitalization, etc. Modern stream reasoning frameworks allow one to model and
solve various real-world problems using incremental and continuous evaluation
of programs as new data arrives in the stream. Applied techniques use, e.g.,
Datalog-like materialization or truth maintenance algorithms to avoid costly
re-computations, thus ensuring low latency and high throughput of a stream
reasoner. However, the expressiveness of existing approaches is quite limited
and, e.g., they cannot be used to encode problems with constraints, which often
appear in practice. In this paper, we suggest a novel approach that uses the
Conflict-Driven Constraint Learning (CDCL) to efficiently update legacy
solutions by using intelligent management of learned constraints. In
particular, we study the applicability of reinforcement learning to
continuously assess the utility of learned constraints computed in previous
invocations of the solving algorithm for the current one. Evaluations conducted
on real-world reconfiguration problems show that providing a CDCL algorithm
with relevant learned constraints from previous iterations results in
significant performance improvements of the algorithm in stream reasoning
scenarios.
Under consideration for acceptance in TPLP.

本文提出了一种基于冲突驱动约束学习（CDCL）的方法，通过智能管理学习到的约束条件来有效地更新流推理算法的遗留解决方案，研究了强化学习在连续评估先前计算的约束条件的实用性，并在真实世界的重构问题上取得了显着的性能提升，为流推理场景提供了新的解决思路。

基于强化学习的流推理缓存策略管理

Managing caching strategies for stream reasoning with reinforcement  learning

Generalising and re-using knowledge learned while solving one problem
instance has been neglected by state-of-the-art answer set solvers. We suggest
a new approach that generalises learned nogoods for re-use to speed-up the
solving of future problem instances. Our solution combines well-known ASP
solving techniques with deductive logic-based machine learning. Solving
performance can be improved by adding learned non-ground constraints to the
original program. We demonstrate the effects of our method by means of
realistic examples, showing that our approach requires low computational cost
to learn constraints that yield significant performance benefits in our test
cases. These benefits can be seen with ground-and-solve systems as well as
lazy-grounding systems. However, ground-and-solve systems suffer from
additional grounding overheads, induced by the additional constraints in some
cases. By means of conflict minimization, non-minimal learned constraints can
be reduced. This can result in significant reductions of grounding and solving
efforts, as our experiments show. (Under consideration for acceptance in TPLP.)

通过结合 ASP 求解技术和演绎逻辑机器学习，我们的新方法能够通过学习约束并将其应用于原始程序以提高求解性能，该方法通过减少冲突最小化，可以减少不必要的求解工作，这是在真实用例中得到证明的。

ASP 中的冲突泛化：学习正确有效的非平庸限制条件

Conflict Generalisation in ASP: Learning Correct and Effective  Non-Ground Constraints

We describe an inductive logic programming (ILP) approach called learning
from failures. In this approach, an ILP system (the learner) decomposes the
learning problem into three separate stages: generate, test, and constrain. In
the generate stage, the learner generates a hypothesis (a logic program) that
satisfies a set of hypothesis constraints (constraints on the syntactic form of
hypotheses). In the test stage, the learner tests the hypothesis against
training examples. A hypothesis fails when it does not entail all the positive
examples or entails a negative example. If a hypothesis fails, then, in the
constrain stage, the learner learns constraints from the failed hypothesis to
prune the hypothesis space, i.e. to constrain subsequent hypothesis generation.
For instance, if a hypothesis is too general (entails a negative example), the
constraints prune generalisations of the hypothesis. If a hypothesis is too
specific (does not entail all the positive examples), the constraints prune
specialisations of the hypothesis. This loop repeats until either (i) the
learner finds a hypothesis that entails all the positive and none of the
negative examples, or (ii) there are no more hypotheses to test. We introduce
Popper, an ILP system that implements this approach by combining answer set
programming and Prolog. Popper supports infinite problem domains, reasoning
about lists and numbers, learning textually minimal programs, and learning
recursive programs. Our experimental results on three domains (toy game
problems, robot strategies, and list transformations) show that (i) constraints
drastically improve learning performance, and (ii) Popper can outperform
existing ILP systems, both in terms of predictive accuracies and learning
times.

该研究介绍了一种基于归纳逻辑编程的学习模型，通过生成、测试和约束三个阶段，不断迭代优化假设（逻辑程序），提高学习性能，其中 Popper 系统在多个领域的实验中表现突出。