Extracting action sequences from texts in natural language is challenging, which requires commonsense inferences based on world knowledge. Although there has been work on extracting action scripts, instructions, navigation actions, etc., they require either the set of candidate actions is provided in advance, or action descriptions are restricted in a specific form, e.g., description templates. In this paper, we aim to extract action sequences from texts in free natural language, i.e., without any restricted templates, provided the candidate set of actions is unknown. We propose to extract action sequences from texts based on the deep reinforcement learning framework. Specifically, we view "selecting" or "eliminating" words from texts as "actions", and texts associated with actions as "states". We then build Q-networks to learn the policy of extracting actions and extract plans from the labelled texts. We exhibit the effectiveness of our approach in several datasets with comparison to state-of-the-art approaches, including online experiments interacting with humans.
The problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for AI Planning. This paper describes LOCM, a system which carries out the automated induction of action schema from sets of example plans. Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a name and a list of objects that the action refers to. LOCM exploits the assumption that actions change the state of objects, and require objects to be in a certain state before they can be executed. The novelty of LOCM is that it can induce action schema without being provided with any information about predicates or initial, goal or intermediate state descriptions for the example action sequences. In this paper we describe the implemented LOCM algorithm, and analyse its performance by its application to the induction of domain models for several domains. To evaluate the algorithm, we used random action sequences from existing models of domains, as well as solutions to past IPC problems. Knowledge about how the world changes over time is a vital component of commonsense knowledge for Artificial Intelligence (AI) and natural language understanding. Actions and events are fundamental components to any knowledge about changes in the state of the world: the states before and after an event differ in regular and predictable ways. We describe a novel system that tackles the problem of extracting knowledge from text about how actions and events change the world over time. We leverage standard language-processing tools, like semantic role labelers and coreference resolvers, as well as large-corpus statistics like pointwise mutual information, to identify STRIPS representations of actions and events, a type of representation commonly used in AI planning systems. In experiments on Web text, our extractor’s Area under the Curve (AUC) improves by more than 31 over the closest system from the literature for identifying the preconditions and add effects of actions. In addition, we also extract significant aspects of STRIPS representations that are missing from previous work, including delete effects and arguments. Most information extraction research identifies the state of the world in text, including the entities and the relationships that exist between them. Much less attention has been paid to the understanding of dynamics, or how the state of the world changes over time. Because intelligent behavior seeks to change the state of the world in rational and utilitymaximizing ways, common-sense knowledge about dynamics is essential for intelligent agents. In this paper, we describe a novel system, PREPOST, that tackles the problem of extracting the preconditions and effects of actions and events, two important kinds of knowledge for connecting world state and the actions that affect it. In experiments on Web text, PREPOST is able to improve by 79 over a baseline technique for identifying the effects of actions (64 improvement for preconditions). We describe a new problem solver called STRIPS that attempts to find a sequence of operators in a spcce of world models to transform a given initial world model into a model in which a given goal formula can be proven to be true. STRIPS represents a world n, del as an arbitrary collection of first-order predicate calculus formulas and is designed to work with .models consisting of large numbers of formulas. It employs a resolution theorem prover to answer questions of particular models and uses means-ends analysis to guide it to the desired goal-satisfying model. Research into knowledge acquisition for robotic agents has looked at interpreting natural language instructions meant for humans into robot-executable programs; however, the ambiguities of natural language remain a challenge for such “translations”. In this paper, we look at a particular sort of ambiguity: the control flow structure of the program described by the natural language instruction. It is not always clear, when more conditional statements appear in a natural language instruction, which of the conditions are to be thought of as alternative options in the same test, and which belong to a code branch triggered by a previous conditional. We augment a system which uses probabilistic reasoning to identify the meaning of the words in a sentence with reasoning about action preconditions and effects in order to filter out non-sensical code structures. We test our system with sample instruction sheets inspired from analytical chemistry. In this paper, we describe an approach for learning planning domain models directly from natural language (NL) descriptions of activity sequences. The modelling problem has been identified as a bottleneck for the widespread exploitation of various technologies in Artificial Intelligence, including automated planners. There have been great advances in modelling assisting and model generation tools, including a wide range of domain model acquisition tools. However, for modelling tools, there is the underlying assumption that the user can formulate the problem using some formal language. And even in the case of the domain model acquisition tools, there is still a requirement to specify input plans in an easily machine readable format. Providing this type of input is impractical for many potential users. This motivates us to generate planning domain models directly from NL descriptions, as this would provide an important step in extending the widespread adoption of planning techniques. We start from NL descriptions of actions and use NL analysis to construct structured representations, from which we construct formal representations of the action sequences. The generated action sequences provide the necessary structured input for inducing a PDDL domain, using domain model acquisition technology. In order to capture a concise planning model, we use an estimate of functional similarity, so sentences that describe similar behaviours are represented by the same planning operator. We validate our approach with a user study, where participants are tasked with describing the activities occurring in several videos. Then our system is used to learn planning domain models using the participants' NL input. We demonstrate that our approach is effective at learning models on these tasks.