No Arabic abstract
Recent research in behaviour understanding through language grounding has shown it is possible to automatically generate behaviour models from textual instructions. These models usually have goal-oriented structure and are modelled with different formalisms from the planning domain such as the Planning Domain Definition Language. One major problem that still remains is that there are no benchmark datasets for comparing the different model generation approaches, as each approach is usually evaluated on domain-specific application. To allow the objective comparison of different methods for model generation from textual instructions, in this report we introduce a dataset consisting of 83 textual instructions in English language, their refinement in a more structured form as well as manually developed plans for each of the instructions. The dataset is publicly available to the community.
This paper presents a two-step generative approach for creating dungeons in the rogue-like puzzle game MiniDungeons 2. Generation is split into two steps, initially producing the architectural layout of the level as its walls and floor tiles, and then furnishing it with game objects representing the players start and goal position, challenges and rewards. Three layout creators and three furnishers are introduced in this paper, which can be combined in different ways in the two-step generative process for producing diverse dungeons levels. Layout creators generate the floors and walls of a level, while furnishers populate it with monsters, traps, and treasures. We test the generated levels on several expressivity measures, and in simulations with procedural persona agents.
Search-based procedural content generation uses stochastic global optimization algorithms to search for game content. However, standard tree search algorithms can be competitive with evolution on some optimization problems. We investigate the applicability of several tree search methods to level generation and compare them systematically with several optimization algorithms, including evolutionary algorithms. We compare them on three different game level generation problems: Binary, Zelda, and Sokoban. We introduce two new representations that can help tree search algorithms deal with the large branching factor of the generation problem. We find that in general, optimization algorithms clearly outperform tree search algorithms, but given the right problem representation certain tree search algorithms perform similarly to optimization algorithms, and in one particular problem, we see surprisingly strong results from MCTS.
Various planning-based know-how logics have been studied in the recent literature. In this paper, we use such a logic to do know-how-based planning via model checking. In particular, we can handle the higher-order epistemic planning involving know-how formulas as the goal, e.g., find a plan to make sure p such that the adversary does not know how to make p false in the future. We give a PTIME algorithm for the model checking problem over finite epistemic transition systems and axiomatize the logic under the assumption of perfect recall.
This paper presents an intelligent price suggestion system for online second-hand listings based on their uploaded images and text descriptions. The goal of price prediction is to help sellers set effective and reasonable prices for their second-hand items with the images and text descriptions uploaded to the online platforms. Specifically, we design a multi-modal price suggestion system which takes as input the extracted visual and textual features along with some statistical item features collected from the second-hand item shopping platform to determine whether the image and text of an uploaded second-hand item are qualified for reasonable price suggestion with a binary classification model, and provide price suggestions for second-hand items with qualified images and text descriptions with a regression model. To satisfy different demands, two different constraints are added into the joint training of the classification model and the regression model. Moreover, a customized loss function is designed for optimizing the regression model to provide price suggestions for second-hand items, which can not only maximize the gain of the sellers but also facilitate the online transaction. We also derive a set of metrics to better evaluate the proposed price suggestion system. Extensive experiments on a large real-world dataset demonstrate the effectiveness of the proposed multi-modal price suggestion system.
In a variety of application settings, the user preference for a planning task - the precise optimization objective - is difficult to elicit. One possible remedy is planning as an iterative process, allowing the user to iteratively refine and modify example plans. A key step to support such a process are explanations, answering user questions about the current plan. In particular, a relevant kind of question is Why does the plan you suggest not satisfy $p$?, where p is a plan property desirable to the user. Note that such a question pertains to plan space, i.e., the set of possible alternative plans. Adopting the recent approach to answer such questions in terms of plan-property dependencies, here we implement a tool and user interface for human-guided iterative planning including plan-space explanations. The tool runs in standard Web browsers, and provides simple user interfaces for both developers and users. We conduct a first user study, whose outcome indicates the usefulness of plan-property dependency explanations in iterative planning.