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Register a new userThe Difficulty of Novelty Detection in Open-World Physical Domains: An Application to Angry Birds
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Added by
Vimukthini Pinto
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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No Arabic abstract
Detecting and responding to novel situations in open-world environments is a key capability of human cognition. Current artificial intelligence (AI) researchers strive to develop systems that can perform in open-world environments. Novelty detection is an important ability of such AI systems. In an open-world, novelties appear in various forms and the difficulty to detect them varies. Therefore, to accurately evaluate the detection capability of AI systems, it is necessary to investigate the difficulty to detect novelties. In this paper, we propose a qualitative physics-based method to quantify the difficulty of novelty detection focusing on open-world physical domains. We apply our method in a popular physics simulation game, Angry Birds. We conduct an experiment with human players with different novelties in Angry Birds to validate our method. Results indicate that the calculated difficulty values are in line with the detection difficulty of the human players.
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Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been developed to solve the complex and challenging physical reasoning problems associated with such a game. However none of these agents attempt one of the key strategies which humans employ to solve Angry Birds levels, which is restarting levels. Restarting is important in Angry Birds because sometimes the level is no longer solvable or some given shot made has little to no benefit towards the ultimate goal of the game. This paper proposes a framework and experimental evaluation for when to restart levels in Angry Birds. We demonstrate that restarting is a viable strategy to improve agent performance in many cases.
This demo paper presents the first system for playing the popular Angry Birds game using a domain-independent planner. Our system models Angry Birds levels using PDDL+, a planning language for mixed discrete/continuous domains. It uses a domain-independent PDDL+ planner to generate plans and executes them. In this demo paper, we present the systems PDDL+ model for this domain, identify key design decisions that reduce the problem complexity, and compare the performance of our system to model-specific methods for this domain. The results show that our systems performance is on par with other domain-specific systems for Angry Birds, suggesting the applicability of domain-independent planning to this benchmark AI challenge.
This paper presents a procedural generation method that creates visually attractive levels for the Angry Birds game. Besides being an immensely popular mobile game, Angry Birds has recently become a test bed for various artificial intelligence technologies. We propose a new approach for procedurally generating Angry Birds levels using Chinese style and Japanese style building structures. A conducted experiment confirms the effectiveness of our approach with statistical significance.
This paper presents an integration of a game system and the art therapy concept for promoting the mental well-being of video game players. In the proposed game system, the player plays an Angry-Birds-like game in which levels in the game are generated based on images they draw. Upon finishing a game level, the player also receives positive feedback (praising words) toward their drawing and the generated level from an Art Therapy AI. The proposed system is composed of three major parts: (1) a drawing recognizer that identifies what object is drawn by the player (Sketcher), (2) a level generator that converts the drawing image into a pixel image, then a set of blocks representing a game level (PCG AI), and (3) the Art Therapy AI that encourages the player and improves their emotion. This paper describes an overview of the system and explains how its major components function.
We investigate the use of attentional neural network layers in order to learn a `behavior characterization which can be used to drive novelty search and curiosity-based policies. The space is structured towards answering a particular distribution of questions, which are used in a supervised way to train the attentional neural network. We find that in a 2d exploration task, the structure of the space successfully encodes local sensory-motor contingencies such that even a greedy local `do the most novel action policy with no reinforcement learning or evolution can explore the space quickly. We also apply this to a high/low number guessing game task, and find that guessing according to the learned attention profile performs active inference and can discover the correct number more quickly than an exact but passive approach.
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