Learning a Planning Domain Model from Natural Language Process Manuals

Artificial intelligence planning techniques have been widely used in many applications. A big challenge is to automate a planning model, especially for planning applications based on natural language (NL) input. This requires the analysis and understanding of NL text and a general learning technique does not exist in real-world applications. In this article, we investigate an intelligent planning technique for natural disaster management, e.g. typhoon contingency plan generation, through natural language process manuals. A planning model is to optimise management operations when a disaster occurs in a short time. Instead of manually building the planning model, we aim to automate the planning model generation by extracting disaster management-related content through NL processing (NLP) techniques. The learning input comes from the published documents that describe the operational process of preventing potential loss in the typhoon management. We adopt a classical planning model, namely planning domain definition language (PDDL), in the typhoon contingency plan generation. We propose a novel framework of FPTCP, which stands for a Framework of Planning Typhoon Contingency Plan , for learning a domain model of PDDL from NL text. We adapt NLP techniques to construct a ternary template of sentences of NL inputs from which actions and their objects are extracted to build a domain model. We also develop a comprehensive suite of user interaction components and facilitate the involvement of users in order to improve the learned domain models. The user interaction is to remove semantic duplicates of NL objects such that the users can select model-generated actions and predicates to better fit the PDDL domain model. We detail the implementation steps of the proposed FPTCP and evaluate its performance on real-world typhoon datasets. In addition, we compare FPTCP with two state-of-the-art approaches in applications of narrative generation, and discuss its capability and limitations

Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation

This paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction. First, a Goals-of-Interest Network (GoINet) is designed to extract the interactive features between agent-to-agent and agent-to-goals using a graph-based vectorized representation. Further, the Conditional Goal Prediction Network (CGPNet) focuses on goal interactive prediction via a combined form of marginal and conditional goal predictors. Finally, the Goaloriented Trajectory Forecasting Network (GTFNet) is proposed to implement trajectory interactive prediction via the conditional goal-oriented predictors, with the predicted future states of the other interacting agent taken as inputs. In addition, a new goal interactive loss is developed to better learn the joint probability distribution over goal candidates between two interacting agents. In the end, the proposed method is conducted on Argoverse motion forecasting dataset, In-house cut-in dataset, and Waymo open motion dataset. The comparative results demonstrate the superior performance of our proposed CGTP model than the mainstream prediction methods.Comment: 14 pages, 4 figure

Modelling other agents through evolutionary behaviours

Modelling other agents is a challenging topic in artificial intelligence research particularly when a subject agent needs to optimise its own decisions by predicting their behaviours under uncertainty. Existing research often leads to a monotonic set of behaviours for other agents so that a subject agent can not cope with unexpected decisions from the other agents. It requires creative ideas about developing diversity of behaviours so as to improve the subject agent’s decision quality. In this paper, we resort to evolutionary computation approaches to generate a new set of behaviours for other agents and solve the complicated agents’ behaviour search and evaluation issues. The new approach starts with the initial behaviours that are ascribed to the other agents and expands the behaviours by using a number of genetic operators in the behaviour evolution. This is the first time that evolutionary techniques are used to modelling other agents in a general multiagent decision framework. We examine the new methods in two well-studied problem domains and provide experimental results in support

Cationic Polybutyl Cyanoacrylate Nanoparticles for DNA Delivery

To enhance the intracellular delivery potential of plasmid DNA using nonviral vectors, we used polybutyl cyanoacrylate (PBCA) and chitosan to prepare PBCA nanoparticles (NPs) by emulsion polymerization and prepared NP/DNA complexes through the complex coacervation of nanoparticles with the DNA. The object of our work is to evaluate the characterization and transfection efficiency of PBCA-NPs. The NPs have a zeta potential of 25.53 mV at pH 7.4 and size about 200 nm. Electrophoretic analysis suggested that the NPs with positive charges could protect the DNA from nuclease degradation and cell viability assay showed that the NPs exhibit a low cytotoxicity to human hepatocellular carcinoma (HepG2) cells. Qualitative and quantitative analysis of transfection in HepG2 cells by the nanoparticles carrying plasmid DNA encoding for enhanced green fluorescent protein (EGFP-N1) was done by digital fluorescence imaging microscopy system and fluorescence-activated cell sorting (FACS). Qualitative results showed highly efficient expression of GFP that remained stable for up to 96 hours. Quantitative results from FACS showed that PBCA-NPs were significantly more effective in transfecting HepG2 cells after 72 hours postincubation. The results of this study suggested that PBCA-NPs have favorable properties for nonviral delivery

Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system

A tag-aware recommender system (TRS) presents the challenge of tag sparsity in a user profile. Previous work focuses on expanding similar tags and does not link the tags with corresponding resources, therefore leading to a static user profile in the recommendation. In this article, we have proposed a new social tag expansion model (STEM) to generate a dynamic user profile to improve the recommendation performance. Instead of simply including most relevant tags, the new model focuses on the completeness of a user profile through expanding tags by exploiting their relations and includes a sufficient set of tags to alleviate the tag sparsity problem. The novel STEM-based TRS contains three operations: (1) Tag cloud generation discovers potentially relevant tags in an application domain; (2) Tag expansion finds a sufficient set of tags upon original tags; and (3) User profile refactoring builds a dynamic user profile and determines the weights of the extended tags in the profile. We analysed the STEM property in terms of recommendation accuracy and demonstrated its performance through extensive experiments over multiple datasets. The analysis and experimental results showed that the new STEM technique was able to correctly find a sufficient set of tags and to improve the recommendation accuracy by solving the tag sparsity problem. At this point, this technique has consistently outperformed state-of-art tag-aware recommendation methods in these extensive experiments

A Connected Components Based Layout Analysis Approach for Educational Documents

Layout analysis, which aims to detect and categorize areas of interest on document images, is an increasingly important part in document image processing. Existing researches have conducted layout analysis on various documents, but none has been proposed for documents yielded from teaching, i.e. exam papers and workbooks, which are worth studying. In this paper, we propose a novel layout analysis system to achieve two tasks for workbook pages and exam papers respectively. On one hand, we segment text and non-text areas of workbook pages. On the other hand, we extract regions of interest on exam papers. Our system is based on connected component (CC) analysis, specifically, it extracts geometric features and spatial information of CCs to recognize page elements. We carried out experiments on images collected from real-world scenarios, and promising results confirmed the applicability and effectiveness of our system

A Comparative Study of Different Information Entropy Index in Personalized Exercise Recommendation

It is meaningful to recommend exercises to students in an online education system with a large amount of learning resource. Many recommendation methods usually rely on strategy in the recommendation system in order to predict an exercise score. In this paper, we compare different information entropy index in the exercise recommendation. These index consider how well exercise matches the student’s knowledge ability. In order to compare different methods, we introduce an interactive platform for dynamic exercise recommendation. We conduct a set of exercise recommendation experiments, compare the effect of different index and find the optimal index in the experiment

A value equivalence approach for solving interactive dynamic influence diagrams

Interactive dynamic influence diagrams (I-DIDs) are recognized graphical models for sequential multiagent decision making under uncertainty. They represent the problem of how a subject agent acts in a common setting shared with other agents who may act in sophisticated ways. The difficulty in solving I-DIDs is mainly due to an exponentially growing space of candidate models ascribed to other agents over time. in order to minimize the model space, the previous I-DID techniques prune behaviorally equivalent models. In this paper, we challenge the minimal set of models and propose a value equivalence approach to further compress the model space. The new method reduces the space by additionally pruning behaviourally distinct models that result in the same expected value of the subject agent’s optimal policy. To achieve this, we propose to learn the value from available data particularly in practical applications of real-time strategy games. We demonstrate the performance of the new technique in two problem domains