I am a Senior Lecturer in Artificial intelligencee at the School of Computing and Information Systems, The University of Melbourne. I’m a member of the Agent Lab group. I completed my PhD at the Artificial Intelligence and Machine Learning Group, Universitat Pompeu Fabra, under the supervision of Prof. Hector Geffner. I was a research fellow for 3 years under the supervision of Prof. Peter Stuckey and Prof. Adrian Pearce, working on solving Mining Scheduling problems through automated planning, constraint programming and operations research techniques.
My research focuses on how to introduce different approaches to the problem of inference in sequential decision problems, as well as applications to autonomous systems.
PhD in Artificial Intelligence, 2012
Universitat Pompeu Fabra
MEng in Artificial Intelligence, 2007
Universitat Pompeu Fabra
BSc in Computer Science, 2004
Universitat Pompeu Fabra
[13/07/20] New paper on Boundary Extension Features for Width-Based Planning with Simulators on Continuous-State Domains, published@IJCAI-20.
[12/05/20] New paper on Goal Recognition Using Off-The-Shelf Process Mining Techniques, published@AAMAS-20.
[31/3/20] New Book Chapter on Planning.Domains published @ Knowledge Engineering Tools and Techniques for AI Planning (KEPS Book)
Width Based Planning searches for solutions through a general measure of state novelty. Performs well over black-box simulators and PDDL problems.
Planimation is a framework to visualise sequential solutions of planning problems specified in PDDL
Awarded top performance classical planners in serveral International Planning Competitions 2008 - 2019
Classical Planners playing Atari 2600 games as well as Deep Reinforcement Learning
classical planners computing infinite loopy plans, and FOND planners synthesizing controllers expressed as policies.
Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation.
The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input language of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems.
The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.
Table of Contents: Praise for An Introduction to the Planning Domain Definition Language / Preface / Introduction / Discrete and Deterministic Planning / More Expressive Classical Planning / Numeric Planning / Temporal Planning / Planning with Hybrid Systems / Conclusion / Bibliography / Authors’ Biographies / Index
Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C; no smoke treatment) and (v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2; R = 0.98; R2 = 0.95; b = 0.97); in berries at harvest (Model 3; R = 0.99; R2 = 0.97; b = 0.96); in wines (Model 4; R = 0.99; R2 = 0.98; b = 0.98). Model 5 was based on the intensity of 12 wine descriptors determined via a consumer sensory test (Model 5; R = 0.98; R2 = 0.96; b = 0.97). These models could be used by winemakers to assess near real-time smoke contamination levels and to implement amelioration strategies to minimize smoke taint in wines following bushfires.
Width-based planning algorithms have been demonstrated to be competitive with state-of-the-art heuristic search and SAT-based approaches, without requiring access to a model of action effects and preconditions, just access to a black-box simulator. Width-based planners search is guided by a measure of the novelty of states, that requires observations on simulator states to be given as a set of features. This paper proposes agnostic feature mapping mechanisms that define the features online, as exploration progresses and the domain of continuous state variables is revealed. We demonstrate the effectiveness of these features on the OpenAI gym “classical control” suite of benchmarks. We compare our online planners with state-of-the-art deep reinforcement learning algorithms, and show that width-based planners using our features can find policies of the same quality with significantly less computational resources.
The problem of probabilistic goal recognition consists of automatically inferring a probability distribution over a range of possible goals of an autonomous agent based on the observations of its behavior. The state-of-the-art approaches for probabilistic goal recognition assume the full knowledge about the world the agent operates in and possible agent’s operations in this world. In this paper, we propose a framework for solving the probabilistic goal recognition problem using process mining techniques for discovering models that describe the observed behavior and diagnosing deviations between the discovered models and observations. The framework imitates the principles of observational learning, one of the core mechanisms of social learning exhibited by humans, and relaxes the above assumptions. It has been implemented in a publicly available tool. The reported experimental results confirm the effectiveness and efficiency of the approach, both for rational and irrational agents’ behaviors.
In this chapter we describe the main pillars of the Planning.Domains initiative (API, Solver, Editor, and Education), detail some of the current use-cases for them, and outline the future path of the initiative. We further dive into some of the most recent developments of Planning.Domains, and shed light on what is next for the platform.
In multi-agent planning, preserving the agents’ privacy has become an increasingly popular research topic. For preserving the agents’ privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, the performance of goal oriented search can be improved by combining goal oriented search and width-based search. The combination of these techniques has been called best-first width search. In this paper, we investigate the usage of best-first width search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents’ privacy and performance. In particular, we show that best-first width search is a very effective approach over several benchmark domains, even when the search is driven by heuristics that roughly estimate the distance from goal states, computed without using the private information of other agents. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art.
Guang Hu [2020 - current], co-supervised with Dr.Tim Miller. Topic: Epistemic Planning and Explanability.
Zihang Su [2020 - current], co-supervised with Dr. Artem Polyvyanyy and Prof. Sebastian Sardina. Topic: Bridging the gap between Process Mining and Goal-Plan-Intention Recognition
Chenyuan Zhang [2020 - current], co-supervised with A/Prof. Charles Kemp (Psychology). Topic: Relaxations and Heuristics in Human Complex Problem Solving
Anubhav Singh [2019 - current], co-supervized with Dr. Miquel Ramirez. Topic: Planing with polynomial guarantees for bounded computational resources
Stefan O'Toole [2018 - current], co-supervized with Dr. Miquel Ramirez and Prof. Adrian Pearce. Topic: Bridging Planning and Reinforcement Learning over continous and discrete dynamics
Chao Lei[2019-current]. Topic: Regression in Classical Planning
Toby Davies, [2013-2017], co-supervized with Prof. Adrian Pearce, Prof. Peter Stuckey and Prof. Harald Sondergaard. Topic: Learning from Conflict in Multi-Agent, Classical, and Temporal Planning. First Employment: Google [2017 - current]. Best Paper Award
ICAPS (2015), Best PhD Thesis,
Melbourne School of Engineering 2018
Dmitry Grebenyuk [2018-2020], co-supervised with Dr. Miquel Ramirez, and Dr. Kris Ehinger. Topic: Agnostic Features for generalized policies computed with Deep Reinforcement Learning. First Employment: Start-up working on Image Processing using DRL.
Guang Hu [2018-2020], co-supervised with Dr.Tim Miller. Topic: What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS.PhD Candidate [2020 - current]
International Conference on Automated Planning and Scheduling – Publicity co-chair,
First Unsolvability International Planning Competition – Co-Organizer,
Heuristics and Search for Domain-independent Planning – Co-Organizer,
ICAPS workshop HSDIP (2015,2016,2017,2018)
Student Abstract track – Co-Chair,
Journal Presentation track – Co-Chair
International Joint Conferences on Artificial Intelligence
Association for the Advancement of Artificial Intelligence,
European Conference on Artificial Intelligence,
International Conference on Automated Planning and Scheduling,
Journal of Artificial Intelligence Research,
Reviewer Artificial Intelligence, Elsevier
Pacman Capture the flag Inter-University Contest, run for Unimelb AI coure and
Hall of Fame contest,
2016 - current
AI Planning for Autonomy (Lecturer), at M.Sc. AI specialization, The University of Melbourne,
2016 - current
Data Structures and Algorithms (Lecturer), at The University of Melbourne,
2016 - current
Software Agents (Lecturer), at M.Sc. Software, The University of Melbourne,
2013, 2014, 2015
Autonomous Systems, at M.Sc. Intelligent Interactive Systems, University Pompeu Fabra,
Advanced course on AI: workshop on RoboSoccer simulator, at Polytechnic School, University Pompeu Fabra,
2009, 2010, 2011
Artificial Intelligence course, at Polytechnic School, University Pompeu Fabra,
Introduction to Data Structures and Algorithms course, at Polytechnic School, University Pompeu Fabra,
Programming course, at Polytechnic School, University Pompeu Fabra,
2008, 2009, 2010, 2011