Search Results for author: Jochen Renz

Found 20 papers, 6 papers with code

Rapid Open-World Adaptation by Adaptation Principles Learning

no code implementations18 Dec 2023 Cheng Xue, Ekaterina Nikonova, Peng Zhang, Jochen Renz

This is an important characteristic of intelligent agents, as it allows them to continue to function effectively in novel or unexpected situations, but still stands as a critical challenge for deep reinforcement learning (DRL).

Physics-Based Task Generation through Causal Sequence of Physical Interactions

no code implementations5 Aug 2023 Chathura Gamage, Vimukthini Pinto, Matthew Stephenson, Jochen Renz

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.

ChatGPT4PCG Competition: Character-like Level Generation for Science Birds

1 code implementation28 Mar 2023 Pittawat Taveekitworachai, Febri Abdullah, Mury F. Dewantoro, Ruck Thawonmas, Julian Togelius, Jochen Renz

An experiment is conducted to determine the effectiveness of several modified versions of this sample prompt on level stability and similarity by testing them on several characters.

Prompt Engineering

NovPhy: A Testbed for Physical Reasoning in Open-world Environments

1 code implementation3 Mar 2023 Chathura Gamage, Vimukthini Pinto, Cheng Xue, Peng Zhang, Ekaterina Nikonova, Matthew Stephenson, Jochen Renz

But is it enough to only have physical reasoning capabilities to operate in a real physical environment?

Don't do it: Safer Reinforcement Learning With Rule-based Guidance

no code implementations28 Dec 2022 Ekaterina Nikonova, Cheng Xue, Jochen Renz

During training, reinforcement learning systems interact with the world without considering the safety of their actions.

reinforcement-learning Reinforcement Learning (RL) +1

Measuring Difficulty of Novelty Reaction

no code implementations28 Jul 2022 Ekaterina Nikonova, Cheng Xue, Vimukthini Pinto, Chathura Gamage, Peng Zhang, Jochen Renz

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.

Phy-Q as a measure for physical reasoning intelligence

1 code implementation31 Aug 2021 Cheng Xue, Vimukthini Pinto, Chathura Gamage, Ekaterina Nikonova, Peng Zhang, Jochen Renz

Inspired by how human IQ is calculated, we define the physical reasoning quotient (Phy-Q score) that reflects the physical reasoning intelligence of an agent using the physical scenarios we considered.

Hi-Phy: A Benchmark for Hierarchical Physical Reasoning

1 code implementation17 Jun 2021 Cheng Xue, Vimukthini Pinto, Chathura Gamage, Peng Zhang, Jochen Renz

In this paper, we propose a new benchmark for physical reasoning that allows us to test individual physical reasoning capabilities.

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

no code implementations16 Jun 2021 Vimukthini Pinto, Cheng Xue, Chathura Nagoda Gamage, Matthew Stephenson, Jochen Renz

Therefore, to accurately evaluate the novelty detection capability of AI systems, it is necessary to investigate how difficult it may be to detect different types of novelty.

Novelty Detection

Deceptive Level Generation for Angry Birds

no code implementations3 Jun 2021 Chathura Gamage, Matthew Stephenson, Vimukthini Pinto, Jochen Renz

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.

Learning Underlying Physical Properties From Observations For Trajectory Prediction

no code implementations25 Sep 2019 Ekaterina Nikonova, Jochen Renz

We show that by using physical laws together with deep learning we achieve a better human-interpretability of learned physical properties, transfer of knowledge to a game with similar physics and very accurate predictions for previously unseen data.

Trajectory Prediction

Using Restart Heuristics to Improve Agent Performance in Angry Birds

1 code implementation30 May 2019 Tommy Liu, Jochen Renz, Peng Zhang, Matthew Stephenson

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.

Support Relation Analysis for Objects in Multiple View RGB-D Images

no code implementations10 May 2019 Peng Zhang, Xiaoyu Ge, Jochen Renz

Understanding physical relations between objects, especially their support relations, is crucial for robotic manipulation.

Object Relation

Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment in Angry Birds

no code implementations7 Feb 2019 Matthew Stephenson, Jochen Renz

This paper presents an adaptive level generation algorithm for the physics-based puzzle game Angry Birds.

A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking

1 code implementation9 Sep 2018 Matthew Stephenson, Damien Anderson, Ahmed Khalifa, John Levine, Jochen Renz, Julian Togelius, Christoph Salge

This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms.

Benchmarking

Towards Explainable Inference about Object Motion using Qualitative Reasoning

no code implementations28 Jul 2018 Xiaoyu Ge, Jochen Renz, Hua Hua

However, there has been no suitable qualitative theory proposed for object motion in three-dimensional space.

The 2017 AIBIRDS Competition

no code implementations14 Mar 2018 Matthew Stephenson, Jochen Renz, Xiaoyu Ge, Peng Zhang

This paper presents an overview of the sixth AIBIRDS competition, held at the 26th International Joint Conference on Artificial Intelligence.

Deceptive Games

no code implementations31 Jan 2018 Damien Anderson, Matthew Stephenson, Julian Togelius, Christian Salge, John Levine, Jochen Renz

Deceptive games are games where the reward structure or other aspects of the game are designed to lead the agent away from a globally optimal policy.

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