Collaborating Authors

Problem Solving



Controlled Natural Languages (CNLs) are effective languages for Knowledge Representation and Reasoning that look like the ones you use every day, so you can easily read and understand them. However, when they are based on Logical AI, meaning behind what is being said can be accurately processed not just by humans but also by machines. As logical CNLs can represent information about the real world in a way that machines can process, you will be able to ensure that meaning of what you write is accurately understood by creating definitions of words yourself or selecting the definitions from pre-defined vocabularies (ontologies). For the first time on any social platform, utility and information will not be lost or neglected, because, by writing in logical CNLs, you will be able to see the overlapping points of agreement, disagreements, and contradictions in the meaning map of all conversations and use logical reasoning to solve complex tasks such as diagnosing a medical condition. Other social platforms (such as Facebook, Twitter, etc.) do not really understand meaning of what you're saying.

Deep Reinforcement Learning for Solving Rubik's Cube


The Rubik's Cube is a famous 3-D puzzle toy. A regular Rubik's Cube has six faces, each of which has nine coloured stickers, and the puzzle is solved when each face has a united colour. If we count one quarter (90) turn as one move and two quarter turns (a "face" turn) as two moves, the best algorithms human-invented can solve any instance of the cube in 26 moves. My target is to let the computer learn how to solve the Rubik's Cube without feeding it any human knowledge like the symmetry of the cube. The most challenging part is the Rubik's Cube has 43,252,003,274,489,856,000 possible permutations.

A Logic-Based Explanation Generation Framework for Classical and Hybrid Planning Problems

Journal of Artificial Intelligence Research

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent’s model. To do so, the agent provides an explanation that can be used to update the model of human such that the agent’s plan is feasible or optimal to the human user. Existing approaches to solve this problem have been based on automated planning methods and have been limited to classical planning problems only. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes. In particular, we propose a logic-based framework for explanation generation, where given a knowledge base KBa (of an agent) and a knowledge base KBh (of a human user), each encoding their knowledge of a planning problem, and that KBa entails a query q (e.g., that a proposed plan of the agent is valid), the goal is to identify an explanation ε ⊆ KBa such that when it is used to update KBh, then the updated KBh also entails q. More specifically, we make the following contributions in this paper: (1) We formally define the notion of logic-based explanations in the context of model reconciliation problems; (2) We introduce a number of cost functions that can be used to reflect preferences between explanations; (3) We present algorithms to compute explanations for both classical planning and hybrid systems planning problems; and (4) We empirically evaluate their performance on such problems. Our empirical results demonstrate that, on classical planning problems, our approach is faster than the state of the art when the explanations are long or when the size of the knowledge base is small (e.g., the plans to be explained are short). They also demonstrate that our approach is efficient for hybrid systems planning problems. Finally, we evaluate the real-world efficacy of explanations generated by our algorithms through a controlled human user study, where we develop a proof-of-concept visualization system and use it as a medium for explanation communication.

Developing safe controllers for autonomous systems under uncertainty


We then define abstract actions that correspond to control inputs that cause transitions between these regions. Due to the noise, every action has multiple possible outcomes that all occur with a certain probability. We compute lower and upper bounds (intervals) on these probabilities based on a finite number of observations of the noise. Our abstraction procedure ensures that we obtain a faithful, yet abstract representation of the autonomous system. In fact, this abstraction constitutes a type of Markov decision process, which is the standard type of model in sequential decision making under uncertainty. To analyze our abstract models in a rigorous manner, we use state-of-art tools from an area called formal verification.

Data Structures: Linked List with Python


From the previous article, we know that arrays help us to store large amounts of data very compactly. But have you ever wondered whether storing large amounts of data could affect the memory of the system?

Russian model who trashed Putin on social media found dead in suitcase: Report

FOX News

Fox News Flash top headlines are here. Check out what's clicking on A Russian model who previously called Vladimir Putin a "psychopath" has been found dead with her body stuffed inside a suitcase, a report says. Gretta Vedler, 23, went missing a year ago after the anti-Putin social media rant, but the two events do not appear to be connected. "Vedler's ex-boyfriend Dmitry Korovin, 23, has now confessed to strangling her to death before driving her 300 miles to the Lipetsk region and abandoning the body in the boot of a car.." the Daily Star reports.

Could Big Data Apps Prevent the Next Pandemic?


For programmers, algorithms and data structures are their most essential subjects--a programmer's bread and butter if you will. If you want to enter the field of programming and hit the ground running, you'll need to master the most common data structures and boost your resume with in-demand skills. Here, we'll explore the eight most important data structures every programmer should know, including what they do and where to use them. To start, let's gain a fundamental understanding of what a data structure is. Data structures are methods of storing and organizing data in a computer system so that operations can be performed upon them more efficiently.

Synopsys Releases Simpleware T-2022.03 for 3D Image Processing, Model Generation


MOUNTAIN VIEW, CA, USA, Mar 9, 2022 – Synopsys is pleased to announce the Simpleware Release T-2022.03. The latest release of Simpleware software includes many new features and improvements, including the new shoulder CT tool in the Simpleware AS Ortho module, contour measurements, improved 3D printing capabilities, and aortic valve analysis. Join us on March 30, 2022 to see the new features in action. Register to watch live or to receive the on-demand recording to view at your own convenience. Synopsys' Simpleware software provides an industry-leading, comprehensive 3D image processing platform for handling 3D scan data.

Pinaki Laskar on LinkedIn: #artificialintelligence #robots #machinelearning


AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How intellect is artificial intelligence today? It is as smart as its dump, dull and deficient (3d). Today's quasi-AI is biased, black box, oblique, weak and narrow. It could blindly and unknowingly perform strictly what it was designed for, videogame/chess/strategic games playing, self-driving, language translation, face recognition, fraud detection, speech communication, product recommendation, pattern matching, generating poetry or music or images or faces or new molecules, etc. It is all relying on statistical relationships in raw input data sets to generate some patterns that humans find useful.