Physics-Based Task Generation through Causal Sequence of Physical Interactions

Performing tasks in a physical environment is a crucial yet challenging problem for AI systems operating in the real world. Physics simulation-based tasks are often employed to facilitate research that addresses this challenge. In this paper, first, we present a systematic approach for defining a physical scenario using a causal sequence of physical interactions between objects. Then, we propose a methodology for generating tasks in a physics-simulating environment using these defined scenarios as inputs. Our approach enables a better understanding of the granular mechanics required for solving physics-based tasks, thereby facilitating accurate evaluation of AI systems' physical reasoning capabilities. We demonstrate our proposed task generation methodology using the physics-based puzzle game Angry Birds and evaluate the generated tasks using a range of metrics, including physical stability, solvability using intended physical interactions, and accidental solvability using unintended solutions. We believe that the tasks generated using our proposed methodology can facilitate a nuanced evaluation of physical reasoning agents, thus paving the way for the development of agents for more sophisticated real-world applications.Comment: The 19th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-23

The Difficulty of Novelty Detection in Open-World Physical Domains: An Application to Angry Birds

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

Comparative assessment of intensive tomato production in innovative non-circulating aquaponics vs. conventional hydroponics

New tendencies in farming techniques which include a composite agricultural production system have evolved as solutions for uninterrupted food supply. Production of high-yielding good-quality tomato (Solanum lycopersicum L.) is one of the leading challenges. This study aimed at evaluating the growth, yield, and fruit quality of hybrid tomato (Umagna), cultivated in non-circulating aquaponics and conventional hydroponics systems. A unique and innovative non-recirculating deep water culture aquaponics system (DWCAS) was developed as a prerequisite for high productivity comparable to current stand-alone fish/plant facilities. Including DWCAS, two other conventional hydroponics systems were compared during the study; the deep water culture hydroponics system (DWCHS) and the open bag system (OBS). The assessment of the production systems was based on the growth behavior, tomato yield, and quality. The maximum yield was observed for the DWCHS followed by DWCAS. The least yield was observed for the OBS. The results demonstrated the highest average fruit weight and marketable yield produced by DWCHS. There was no difference in plant dry matter content among production systems. The fertilizer use efficiency was increased by 11.7% and 85.86% in favor of the DWCHS and DWCAS, respectively.  The total rainwater use efficiency was also increased in DWCHS

Measuring Difficulty of Novelty Reaction

Current AI systems are designed to solve close-world problems with the assumption that the underlying world is remaining more or less the same. However, when dealing with real-world problems such assumptions can be invalid as sudden and unexpected changes can occur. To effectively deploy AI-powered systems in the real world, AI systems should be able to deal with open-world novelty quickly. Inevitably, dealing with open-world novelty raises an important question of novelty difficulty. Knowing whether one novelty is harder to deal with than another, can help researchers to train their systems systematically. In addition, it can also serve as a measurement of the performance of novelty robust AI systems. In this paper, we propose to define the novelty reaction difficulty as a relative difficulty of performing the known task after the introduction of the novelty. We propose a universal method that can be applied to approximate the difficulty. We present the approximations of the difficulty using our method and show how it aligns with the results of the evaluation of AI agents designed to deal with novelty

Distributed-Proof-of-Sense: Blockchain Consensus Mechanisms for Detecting Spectrum Access Violations of the Radio Spectrum

The exponential growth in connected devices with Internet-of-Things (IoT) and next-generation wireless networks requires more advanced and dynamic spectrum access mechanisms. Blockchain-based approaches to Dynamic Spectrum Access (DSA) seem efficient and robust due to their inherited characteristics such as decentralization, immutability and transparency. However, conventional consensus mechanisms used in blockchain networks are expensive to be used due to the cost, processing and energy constraints. Moreover, addressing spectrum violations (i.e., unauthorized access to the spectrum) is not well-discussed in most blockchain-based DSA systems in the literature. In this work, we propose a newly tailored energyefficient consensus mechanism called “Distributed-Proof-of-Sense (DPoS)” that is specially designed to enable DSA and detect spectrum violations. The proposed consensus algorithm motivates blockchain miners to perform spectrum sensing, which leads to the collection of a full spectrum of sensing data. An elliptic curve cryptography-based zero-knowledge proof is used as the core of the proposed mechanism. We use MATLAB simulations to analyze the performance of the consensus mechanism and implement several consensus algorithms in a microprocessor to highlight the benefits of adopting the proposed system

Physics-Based Task Generation through Causal Sequence of Physical Interactions

Performing tasks in a physical environment is a crucial yet challenging problem for AI systems operating in the real world. Physics simulation-based tasks are often employed to facilitate research that addresses this challenge. In this paper, first, we present a systematic approach for defining a physical scenario using a causal sequence of physical interactions between objects. Then, we propose a methodology for generating tasks in a physics-simulating environment using these defined scenarios as inputs. Our approach enables a better understanding of the granular mechanics required for solving physics-based tasks, thereby facilitating accurate evaluation of AI systems' physical reasoning capabilities. We demonstrate our proposed task generation methodology using the physics-based puzzle game Angry Birds and evaluate the generated tasks using a range of metrics, including physical stability, solvability using intended physical interactions, and accidental solvability using unintended solutions. We believe that the tasks generated using our proposed methodology can facilitate a nuanced evaluation of physical reasoning agents, thus paving the way for the development of agents for more sophisticated real-world applications

Deceptive Level Generation for Angry Birds

The Angry Birds AI competition has been held over many years to encourage the development of AI agents that can play Angry Birds game levels better than human players. Many different agents with various approaches have been employed over the competition's lifetime to solve this task. Even though the performance of these agents has increased significantly over the past few years, they still show major drawbacks in playing deceptive levels. This is because most of the current agents try to identify the best next shot rather than planning an effective sequence of shots. In order to encourage advancements in such agents, we present an automated methodology to generate deceptive game levels for Angry Birds. Even though there are many existing content generators for Angry Birds, they do not focus on generating deceptive levels. In this paper, we propose a procedure to generate deceptive levels for six deception categories that can fool the state-of-the-art Angry Birds playing AI agents. Our results show that generated deceptive levels exhibit similar characteristics of human-created deceptive levels. Additionally, we define metrics to measure the stability, solvability, and degree of deception of the generated levels

A simulation framework for a real-time demand responsive public transit system

Transit systems have encountered a radical change in the recent past as a result of the digital disruption. Consequently, traditional public transit systems no longer satisfy the diversified demands of passengers and hence, have been complemented by demand responsive transit solutions. However, we identify a lack of simulation tools developed to test and validate complex scenarios for real-time demand responsive public transit. Thus, in this paper, we propose a simulation framework, which combines complex scenario creation, optimization algorithm execution and result visualization using SUMO, an open source continuous simulator. In comparison to a state-of-the-art work, the proposed tool supports features such as varying vehicle capacity and driving range, immediate and advance passenger requests and maximum travel time constraints. Further, the framework follows a modular architecture that allows plug-and-play support for external modules