I am an Associate Professor in Artificial intelligencee at the School of Computing and Information Systems, The University of Melbourne. I’m a member of the Agent Lab group and the Digital Agriculture, Food and Wine lab.
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 in agriculture.
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.
Graduate Certificate in University Teaching, 2020
The University of Melbourne
PhD in Artificial Intelligence, 2012
Universitat Pompeu Fabra
MEng in Artificial Intelligence, 2007
Universitat Pompeu Fabra
BSc in Computer Science, 2004
Universitat Pompeu Fabra
[3/24] The University of Melbourne, Theaching Video for Algorithms and Data Structures
[2/24] New AAAI-24 Paper on Generalized Planning for the Abstraction and Reasoning Corpus
[1/24] The Guardian newspaper featured Farmbots, flavour pills and zero-gravity beer: inside the mission to grow food in space, and shared too through Farm.bot’s official newsletter
[12/23] TV Channel 10 News featured the collaborative projects on plants for Space as well as a new short video released by The University of Melbourne
[12/23] Pursuit media article You can’t explore the solar system on an empty stomach, featuring the integration of new sensors over Farm.bots
Planning as a Service (PaaS) is an extendable API to deploy planners online in local or cloud servers
Farm.bot is an open-source robotic platform to explore problems on AI and Automation (Planning, Vision, Learning) for small scale …
Width Based Planning searches for solutions through a general measure of state novelty. Performs well over black-box simulators and …
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
Invariants, Traps, Un-reachability Certificates, and Dead-end Detection
Software to support AI courses in Mel & RMIT Unis (Melbourne, AUS)
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.
Lightweight Automated Planning ToolKiT (LAPKT) to build, use or extend basic to advanced Automated Planners
Width-based algorithms search for solutions through a general definition of state novelty. These algorithms have been shown to result in state-of-the-art performance in classical planning, and have been successfully applied to model-based and model-free settings where the dynamics of the problem are given through simulation engines. Width-based algorithms performance is understood theoretically through the notion of planning width, providing polynomial guarantees on their runtime and memory consumption. To facilitate synergies across research communities, this paper summarizes the area of width-based planning, and surveys current and future research directions.
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
The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that poses difficulties for pure machine learning methods due to its requirement for fluid intelligence with a focus on reasoning and abstraction. In this work, we introduce an ARC solver, Generalized Planning for Abstract Reasoning (GPAR). It casts an ARC problem as a generalized planning (GP) problem, where a solution is formalized as a planning program with pointers. We express each ARC problem using the standard Planning Domain Definition Language (PDDL) coupled with external functions representing object-centric abstractions. We show how to scale up GP solvers via domain knowledge specific to ARC in the form of restrictions over the actions model, predicates, arguments and valid structure of planning programs. Our experiments demonstrate that GPAR outperforms the state-of-the-art solvers on the object-centric tasks of the ARC, showing the effectiveness of GP and the expressiveness of PDDL to model ARC problems. The challenges provided by the ARC benchmark motivate research to advance existing GP solvers and understand new relations with other planning computational models.
A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand. Active prostheses utilize real-valued, continuous sensor data to recognize patient target poses, or goals, and proactively move the artificial limb. Previous studies have examined how well the data collected in stationary poses, without considering the time steps, can help discriminate the goals. In this case study paper, we focus on using time series data from surface electromyography electrodes and kinematic sensors to sequentially recognize patients' goals. Our approach involves transforming the data into discrete events and training an existing process mining-based goal recognition system. Results from data collected in a virtual reality setting with ten subjects demonstrate the effectiveness of our proposed goal recognition approach, which achieves significantly better precision and recall than the state-of-the-art machine learning techniques and is less confident when wrong, which is beneficial when approximating smoother movements of prostheses.
The problem of goal recognition requests to automatically infer an accurate probability distribution over possible goals an autonomous agent is attempting to achieve in the environment. The state-of-the-art approaches for goal recognition operate under full knowledge of the environment and possible operations the agent can take. This knowledge, however, is often not available in real-world applications. Given historical observations of the agents' behaviors in the environment, we learn skill models that capture how the agents achieved the goals in the past. Next, given fresh observations of an agent, we infer their goals by diagnosing deviations between the observations and all the available skill models. We present a framework that serves as an outline for implementing such data-driven goal recognition systems and its instance system implemented using process mining techniques. The evaluations we conducted using our publicly available implementation confirm that the approach is well-defined, i.e., all system parameters impact its performance, has high accuracy over a wide range of synthetic and real-world domains, which is comparable with the more knowledge-demanding state-of-the-art approaches, and operates fast.
Diverse, top-k, and top-quality planning are concerned with the generation of sets of solutions to sequential decision problems. Previously this area has been the domain of classical planners that require a symbolic model of the problem instance. This paper proposes a novel alternative approach that uses Monte Carlo Tree Search (MCTS), enabling application to problems for which only a black-box simulation model is available. We present a procedure for extracting bounded sets of plans from pre-generated search trees in best-first order, and a metric for evaluating the relative quality of paths through a search tree. We demonstrate this approach on a path-planning problem with hidden information, and suggest adaptations to the MCTS algorithm to increase the diversity of generated plans. Our results show that our method can generate diverse and high-quality plan sets in domains where classical planners are not applicable.
Understanding problem solving or planning has been a shared challenge for both AI and cognitive science since the birth of both fields. We explore the extent to which modern planners from the field of AI can account for human performance on the Tower of London (TOL) task, a close relative of the Tower of Hanoi problem that has been extensively studied by psychologists. We characterize the task using the Planning Domain Definition Language (PDDL) and evaluate an adaptive online planner and a family of well-known planners, including online planners, optimal planners and satisficing planners. Each planner is evaluated based on its ability to predict the actions and planning times of participants in a new behavioral experiment. Our results suggest that participants use a range of strategies but that an adaptive lookahead planner provides the best overall account of both human actions and human planning times. This finding is consistent with the view that humans differ from standard AI planners by integrating a mechanism for evidence accumulation.
Ciao Lei
[2022 - current]. co-supervised with Dr. Kris Ehinger and A/Prof Sigfredo Fuentes, Topic: Generalized vision planning problems and their applications in Agriculture
Zhiaho Pei
[2022 - current]. co-supervized with Dr. Angela Rojas, Dr. Fjalar De Haan and Dr. Enayat A. Moallemi, Topic: Robust decision making for complex systems
Sukai Huang
[2022 - current]. co-supervized with Prof. Trevor Cohn, Topic: NLP and sequential decision problems
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 - 2022], co-supervized with Dr. Miquel Ramirez and Prof. Adrian Pearce. Thesis:
The Intersection of Planning and Learning through Cost-to-go Approximations, Imitation and Symbolic Regression First Employment: Meta [2022 - current]
Toby Davies
, [2013-2017], co-supervized with Prof. Adrian Pearce, Prof. Peter Stuckey and Prof. Harald Sondergaard. Thesis:
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
Zhiaho Pei
[2021]. co-supervized with Dr. Angela Rojas, Dr. Fjalar De Haan and Dr. Enayat A. Moallemi, Thesis:
Robust decision making for complex systems
Marco Marasco
[2021]. co-supervized with Dr. Angela Rojas, Dr. Fjalar De Haan and Dr. Enayat A. Moallemi, Thesis:
Adaptive Policy making for systems of electricity provision
Jiayuan Chang
[2021]. co-supervized with A/Prof Sigfredo Fuentes, Thesis:
FarmBot.io Automated Planning: simulation and integration
Yajing Ma
[2021]. co-supervized with A/Prof Sigfredo Fuentes, Thesis:
Electronic Nose for pest detection
Dmitry Grebenyuk
[2018-2020], co-supervised with Dr. Miquel Ramirez, and Dr. Kris Ehinger. Thesis:
Agnostic Features for generalized policies computed with Deep Reinforcement Learning (DRL). First Employment: Start-up working on Image Processing using DRL
Guang Hu
[2018-2020], co-supervised with Dr.Tim Miller. Thesis:
What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS. PhD Candidate [2020 - current]
Ciao Lei
[2019-2020]. Thesis:
Regression and Width in Classical Planning and
ICAPS21 paper
ICAPS (2025)
ICAPS (2019)
International Conference on Automated Planning and Scheduling – Publicity co-chair, ICAPS (2010)
First Unsolvability International Planning Competition – Co-Organizer, UIPC-1 (2016)
Heuristics and Search for Domain-independent Planning – Co-Organizer, ICAPS workshop HSDIP (2015,2016,2017,2018)
Demonstration track – Co-Chair AAAI 2023
Student Abstract track – Co-Chair, AAAI (2018,2019)
Journal Presentation track – Co-Chair ICAPS (2018)
AAAI (2020,2021,2022,2023)
IJCAI (2021,2023)
International Joint Conferences on Artificial Intelligence IJCAI (2011,2013,2015,2017,2018,2020,2022)
Association for the Advancement of Artificial Intelligence, AAAI (2013,2015,2016,2017,2018,2019)
European Conference on Artificial Intelligence, ECAI (2014,2016)
International Conference on Automated Planning and Scheduling, ICAPS (2015,2016,2017,2018,2020)
Symposium on Combinatorial Search SOCS (2020,2021,2022,2023)
Journal of Artificial Intelligence Research, JAIR
Reviewer Artificial Intelligence, Elsevier AIJ
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, 2012
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, 2010, 2011
Introduction to Data Structures and Algorithms course
, at Polytechnic School, University Pompeu Fabra, 2008
Programming course
, at Polytechnic School, University Pompeu Fabra, 2008, 2009, 2010, 2011