New Zealand


As reinforcement learning (RL) continues to improve and be applied in situations alongside humans, the need to explain the learned behaviors of RL agents to end-users becomes more important. Strategies for explaining the reasoning behind an agent&#39;s policy, called policy-level explanations, can lead to important insights about both the task and the agent&#39;s behaviors. Following this line of research, in this work, we propose a novel approach that summarizes an agent&#39;s policy in the form of a directed graph with natural language descriptions. A decision tree based clustering method is utilized to abstract the state space of the task into fewer, condensed states which makes the policy graphs more digestible to end-users. This abstraction allows the users to control the size of the policy graph to achieve their desired balance between comprehensibility and accuracy. In addition, we develop a heuristic optimization method to find the most explainable graph policy and present it to the users. Finally, we use the user-defined predicates to enrich the abstract states with semantic meaning. We test our approach on five RL tasks, using both deterministic and stochastic policies, and show that our method is: (1) agnostic to the algorithms used to train the policies, and (2) comparable in accuracy and superior in explanation capabilities to existing baselines. Especially, when provided with our explanation graph, end-users are able to accurately interpret policies of trained RL agents 80% of the time, compared to 10% when provided with the next best baseline. We uploaded our code and datasets as supplementary material for ensuring the reproducibility of our research findings.

AAMAS 2022

CAPS: Comprehensible Abstract Policy Summaries for Explaining Reinforcement Learning Agents

human-agent interaction

explainability

reinforcement learning

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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https://aamas2022-conference.auckland.ac.nz/attending/registration/

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AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).


As reinforcement learning (RL) continues to improve and be applied in situations alongside humans, the need to explain the learned behaviors of RL agents to end-users becomes more important. Strategies for explaining the reasoning behind an agent's policy, called policy-level explanations, can lead to important insights about both the task and the agent's behaviors. Following this line of research, in this work, we propose a novel approach that summarizes an agent's policy in the form of a directed graph with natural language descriptions. A decision tree based clustering method is utilized to abstract the state space of the task into fewer, condensed states which makes the policy graphs more digestible to end-users. This abstraction allows the users to control the size of the policy graph to achieve their desired balance between comprehensibility and accuracy. In addition, we develop a heuristic optimization method to find the most explainable graph policy and present it to the users. Finally, we use the user-defined predicates to enrich the abstract states with semantic meaning. We test our approach on five RL tasks, using both deterministic and stochastic policies, and show that our method is: (1) agnostic to the algorithms used to train the policies, and (2) comparable in accuracy and superior in explanation capabilities to existing baselines. Especially, when provided with our explanation graph, end-users are able to accurately interpret policies of trained RL agents 80% of the time, compared to 10% when provided with the next best baseline. We uploaded our code and datasets as supplementary material for ensuring the reproducibility of our research findings.

technical paper


We address the problem of routing a team of drones and trucks over large-scale urban road networks. To conserve their limited flight energy, drones can use trucks as temporary modes of transit en route to their own destinations. Such coordination can yield significant savings in total vehicle distance traveled, i.e., truck travel distance and drone flight distance, compared to operating drones and trucks independently. But it comes at the potentially prohibitive computational cost of deciding which trucks and drones should coordinate and when and where it is most beneficial to do so. We tackle this fundamental trade-off by decoupling our overall intractable problem into tractable sub-problems that we solve stage-wise. The first stage solves only for trucks, by computing paths that make them more likely to be useful transit options for drones. The second stage solves only for drones, by routing them over a composite of the road network and the transit network defined by truck paths from the first stage. We design a comprehensive algorithmic framework that frames each stage as a multi-agent path finding problem and implement two distinct methods for solving them. We evaluate our approach on extensive simulations with up to 100 agents on the real-world Manhattan road network containing nearly 4500 vertices and 10000 edges. Our framework saves on more than 50% of vehicle distance traveled compared to independently solving for trucks and drones, and computes solutions for all settings within 5 minutes on commodity hardware.

Coordinated Multi-Agent Path Finding for Drones and Trucks over Road Networks


Formal models of multi-robot behaviour are fundamental to planning, simulation, and model checking techniques. However, existing models are invalidated by strong assumptions that fail to capture execution-time multi-robot behaviour, such as simplistic duration models or synchronisation constraints. In this paper we propose a novel multi-robot Markov automaton formulation which models asynchronous multi-robot execution in continuous time. Robot dynamics are captured using phase-type distributions over action durations. Moreover, we explicitly model the effects of robot interactions, as they are a key factor for the duration of action execution. We also present a scalable discrete-event simulator which yields realistic statistics over execution-time robot behaviour by sampling through the Markov automaton. We validate our model and simulator against a Gazebo simulation in a range of multi-robot navigation scenarios, demonstrating that our model accurately captures high-level multi-robot behaviour.

Context-Aware Modelling for Multi-Robot Systems Under Uncertainty


To become helpful assistants in our daily lives, robots must be able to understand the effects of their actions on their environment. A modern approach to this is the use of a physics simulation, where often very general simulation engines are utilized. As a result, specific modeling features, such as multi-contact simulation or fluid dynamics, may not be well represented. To improve the representativeness of simulations, we propose a framework for combining estimations of multiple heterogeneous simulations into a single one. The framework couples multiple simulations and reorganizes them based on semantically annotated action sequence information. While each object in the scene is always covered by a simulation, this simulation responsibility can be reassigned online. In this paper, we introduce the concept of the framework, describe the architecture, and demonstrate two example implementations. Eventually, we demonstrate how the framework can be used to extract semantic information from action executions on the humanoid robot Rollin’ Justin and how this information is used to assess whether an action sequence is executed successful or not.

Multi-Agent Heterogeneous Digital Twin Framework with Dynamic Responsibility Allocation for Complex Task Simulation


We study the computational complexity of multi-agent path finding (MAPF) on grid graphs.
Given a graph $G$ and a set of agents, each having a start and target vertex, the goal is to find collision-free paths minimizing the total distance traveled.
Formulations restricting $G$ to be a 2D grid are ubiquitous, as it conveniently allows for modeling well-structured environments (e.g., warehouses).
Previous hardness results had $G$ either (1) be a 2D grid or (2) have more than one vertex unoccupied by a agent, but never considered both properties simultaneously.
The most restricted result for property (2) was shown for (non-grid) planar graphs.
We refine previous results by proving that distance-optimal MAPF remains NP-hard for both properties simultaneously, thereby settling an open problem posed in Banfi et al. (2017).
We present a reduction directly from 3SAT using simple gadgets, making our proof arguably more comprehensible and informative.
We explain the potential benefits of these qualities towards positive results.
Furthermore, our result is the first linear-time/size reduction for the case where $G$ is planar, appearing nearly four decades after the first related result.
This allows us to go a step further and exploit the Exponential Time Hypothesis (ETH) to obtain an exponential lower bound for the running time.
To the best of our knowledge, this is the first application of ETH to show \textit{any} concrete lower bound for MAPF.
Finally, as a stepping stone, we also show hardness for the monotone case, in which agents move one by one with no intermediate stops.

Refined Hardness of Distance-Optimal Multi-Agent Path Finding


Ineffective traffic signal control is one of the major causes of congestion in urban road networks. Dynamically changing traffic conditions and live traffic state estimation are fundamental challenges that limit the ability of the existing signal infrastructure in rendering individualized signal control in real-time. We use deep reinforcement learning (DRL) to address these challenges. Due to economic and safety constraints associated training such agents in the real world, a practical approach is to do so in simulation before deployment. Domain randomisation is an effective technique for bridging the \textit{reality gap} and ensuring effective transfer of simulation-trained agents to the real world. In this paper, we develop a fully-autonomous, vision-based DRL agent that achieve adaptive signal control in the face of complex, imprecise, and dynamic traffic environments. Our agent uses live visual data from an intersection to extensively perceive and subsequently act upon the traffic environment. Employing domain randomisation, we examine our agent's generalisation capabilities under varying traffic conditions in both the simulation and the real-world environments. In a diverse validation set independent of training data, our traffic control agent reliably adapted to novel traffic situations and demonstrated a positive transfer to previously unseen real intersections despite being trained entirely in simulation.

Fully-Autonomous, Vision-based Traffic Signal Control: from Simulation to Reality


On-demand ride-pooling (e.g., UberPool, GrabShare) services focus on serving multiple different customer requests using each vehicle, i.e., an empty or partially filled vehicle can be assigned requests from different passengers with different origins and destinations. On the other hand, in Taxi on Demand (ToD) services (e.g., UberX), one vehicle is assigned to only one request at a time. On-demand ride pooling is not only beneficial to customers (lower cost), drivers (higher revenue per trip) and aggregation companies (higher revenue), but is also of crucial importance to the environment as it reduces the number of vehicles required on the roads. 

Since each vehicle has to be matched with a combination of customer requests, the matching problem in ride pooling is significantly more challenging. Due to this complexity, most existing solutions to ride-pooling problem are myopic in that they either ignore future impact of current matches or the impact of other taxis in the expected revenue earned by a taxi. In this paper, we build on an approximate dynamic programming framework to consider impact of other taxis on the value of a taxi (expected revenue earned until end of horizon) through a novel hierarchical value decomposition framework. On a real world city scale taxi data set, we show an improvement of up to 10\% in requests served compared to existing best methods for ride pooling.

Hierarchical Value Decomposition for Effective On-demand Ride-Pooling


In this paper, we explore a multiagent-based deconfliction strategy in Unmanned Air Traffic Management (UTM), as to equip autonomous unmanned aerial vehicles (UAV) with decision capabilities to update their trajectories (4D contracts) when facing unpredictable events or when priority trajectories are added to the flight space. Indeed, current initiatives for UTM envision a multi-layered management system where the lowest one is composed of either autonomous UAV or human-operated one, that may have to react to incidents and emergencies, while sticking to the contracted 4D trajectories. We propose here an agent-based approach, where UAVs, being aware of close potential conflicts provided by the UTM communication layer, can negociate directly, without central UTM control, to update trajectories and solve resulting conflicts in a decentralized manner, as to avoid access to a central decision bottleneck and to provide reactive deconfliction. We propose both uncoordinated and coordinated behaviors, that we experimentally evaluate on dense scenarios within a limited area with numerous 4D contracts, potential incidents and emergency procedures.

Trajectory Coordination based on Distributed Constraint Optimization Techniques in Unmanned Air Traffic Management


The popularity of mobility-on-demand (MoD) systems boosts the need for online collective multiagent planning, where spatially distributed servicing agents are planned to meet dynamically arriving demands. For city-scale MoDs with a population of agents, it is necessary to find a balance between computation time (i.e., real-time) and solution quality (i.e., the number of demands served). Directly using an offline policy can guarantee real-time, but cannot be dynamically adjusted to real agent and demand distributions. On the other hand, search-based online planning methods are adaptive. However, they are computationally expensive and cannot scale up.

In this paper, we propose a principled online multiagent planning method, which reuses and improves the offline policy in an anytime manner. We first model MoDs as a collective Markov Decision Process ($\mathbb{C}$-MDP) where the history collective behavior of agents affects the joint reward. We propose a novel state value function to evaluate the policy, and a gradient ascent (GA) technique to improve the policy. We show that GA-based policy iteration (GA-PI) on local policy can converge to global optima of $\mathbb{C}$-MDP. Finally, given real-time information, the offline policy is used as the default plan and GA-PI is used to improve it and generate an online plan. Experimentally, the proposed offline policy reuse method significantly outperforms standard online multiagent planning methods on MoD systems like ride-sharing and security traffic patrolling in terms of computation time and solution quality.

Online Collective Multiagent Planning by Offline Policy Reuse with Applications to City-Scale Mobility-on-Demand Systems


The use of norms to guide and coordinate interactions has gained tremendous attention in the multiagent community. However, as the interest moves towards dynamic socio-technical systems, where human and software agents interact and interactions are required to adapt to the human's changing needs, new challenges arise. For instance, different agents (human or software) might not have the same understanding of what it means to violate a norm (e.g., what characterizes hate speech), or that their understanding of a norm might change over time (e.g., what constitutes an acceptable response time). The challenge is to address these issues by learning the meaning of a norm violation from the limited interaction data. For this, we propose a framework that learns and updates the meaning of a norm violation from interaction data through a combination of ensemble and incremental learning techniques. Ensemble learning handles the imbalance class distribution of the interaction stream, and incremental learning is used to continuously update the ensemble models as community members interact, which is an essential feature to keep the ensemble models in accordance with the latest community view on the meaning of norm violation. We evaluate the proposed approach in the use case of Wikipedia article edits, where interactions revolve around editing articles and the norm in question is prohibiting vandalism. Results show that the proposed framework can learn the meaning of a norm violation in a setting with data imbalance and concept drift.

Ensemble and Incremental Learning for Norm Violation Detection


The evolving relationship between humans and technology entails increasing concerns about the impact on ethical issues such as bias, unfairness, and lack of accountability. There is thus a need for consistent responses to multiple-user social dilemma that arise during interactions in sociotechnical systems, where combinations of humans and technical agents work together as ethical duos. The outcomes of these systems can be evaluated by the values that participating humans hold, which in turn influence the development of norms used to guide acceptable behaviour. However, when values are misjudged or norms conflict, systematic methodologies are needed to resolve the dilemmas that arise in satisfactory ways.

Adapting a macro-ethics perspective, in which ethics is addressed through the governance of sociotechnical systems with multiple agents rather than the actions of single agents, we propose that ethical principles can be mechanised in a way that considers a variety of contextual inputs to produce a satisfactory outcome. The application of these ethical principles would enable practitioners to think analytically and systematically about the multiple-user social dilemmas that occur in these systems to resolve them in satisfactory ways. In order to achieve this, we need to formulate new ways of (1) developing consistent responses to multiple-user social dilemmas, (2) categorizing ethical principles used in AI, and (3) systematically operationalizing ethical principles.

CAPS: Comprehensible Abstract Policy Summaries for Explaining Reinforcement Learning Agents

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Coordinated Multi-Agent Path Finding for Drones and Trucks over Road Networks

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CAPS: Comprehensible Abstract Policy Summaries for Explaining Reinforcement Learning Agents

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Next from AAMAS 2022

Coordinated Multi-Agent Path Finding for Drones and Trucks over Road Networks

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Explainability in Multi-Agent Path/Motion Planning: User-study-driven taxonomy and requirements

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