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Given the complexity of real-world, unstructured domains, it is often impossible or impractical to design models that include every feature needed to handle all possible scenarios that an autonomous system may encounter. For an autonomous system to be reliable in such domains, it should have the ability to improve its competence online. In this paper, we propose a method for improving the competence of a system over the course of its deployment. We specifically focus on a class of semi-autonomous systems known as competence-aware systems that model their own competence---the optimal extent of autonomy to use in any given situation---and learn this competence over time from feedback received through interactions with a human authority. Our method exploits such feedback to identify important state features missing from the system&#39;s initial model, and incorporates them into its state representation. The result is an agent that better predicts human involvement, leading to improvements in its competence and reliability, and as a result, its overall performance.

ECAI 2020

Improving Competence for Reliable Autonomy

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS 2020

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Given the complexity of real-world, unstructured domains, it is often impossible or impractical to design models that include every feature needed to handle all possible scenarios that an autonomous system may encounter. For an autonomous system to be reliable in such domains, it should have the ability to improve its competence online. In this paper, we propose a method for improving the competence of a system over the course of its deployment. We specifically focus on a class of semi-autonomous systems known as competence-aware systems that model their own competence---the optimal extent of autonomy to use in any given situation---and learn this competence over time from feedback received through interactions with a human authority. Our method exploits such feedback to identify important state features missing from the system's initial model, and incorporates them into its state representation. The result is an agent that better predicts human involvement, leading to improvements in its competence and reliability, and as a result, its overall performance.

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

"Autonomous systems developed with the Belief-Desire-Intention (BDI) architecture are usually mostly implemented in simulated environments. In this project we sought to build a BDI agent for use in the real world for campus mail delivery in the tunnel system at Carleton University.  Ideally, the robot should receive a delivery order via a mobile application, pick up the mail at a station, navigate the tunnels to the destination station, and notify the recipient. We linked the Robot Operating System (ROS) with a BDI reasoning system to achieve a subset of the required use cases. ROS handles the low-level sensing and actuation, while the BDI reasoning system handles the high-level reasoning and decision making. Sensory data is orchestrated and sent from ROS to the reasoning system as perceptions. These perceptions are then deliberated upon, and an action string is sent back to ROS for interpretation and driving of the necessary actuator for the action to be performed.

In this paper we present our current implementation, which closes the loop on the hardware-software integration, and implements a subset of the use cases required for the full system."

Toward Campus Mail Delivery using BDI

Knowing when a driver will quit cruising and either leave the area or park at an expensive off-street facility is critical for modeling parking search. We employ a serious game – PARKGAME for estimating the dynamics of drivers’ decision making. 49 Participants of a game experiment were involved in three scenarios where they had to arrive on time to a fictional appointment or face monetary penalties, and to choose between uncertain but cheap on-street parking or a certain but costly parking lot. Scenarios diverged on the time to appointment and distance between the meeting place and parking lot locations. Players played a series of 8 or 16 computer games on a Manhattan grid road network with high on-street parking occupancy and nearby parking lot of unlimited capacity. Players’ choices to quit or to continue search, as dependent on the search time, were analyzed with an accelerated-failure time (AFT) model. Results show that drivers are mostly risk-averse and quit on-street parking search very soon after potential loses begin to accumulate. The implications of game-based methods for simulation model development and sustainable parking policy are further discussed.

To quit or to cruise? Modeling parking search decisions in serious games

Enabling Ride-sharing (RS) in existing Mobility-on-demand (MoD) systems allows to reduce the operating vehicle fleet size while achieving a similar level of service. This however requires an efficient vehicle to multiple requests assignment, which is the focus of most RS-related research, and an adaptive fleet rebalancing strategy, which counteracts the uneven geographical spread of demand and relocates unoccupied vehicles to the areas of higher demand. Existing research into rebalancing generally divides the system coverage area into predefined geographical zones, however, this is done statically at design-time and can limit their adaptivity to evolving demand patterns. To enable dynamic, and therefore more accurate rebalancing, this paper proposes a Dynamic Demand-Responsive Rebalancer (D2R2) for RS systems. D2R2 uses Expectation-Maximization (EM) clustering to determine relocation zones at runtime. D2R2 recalculates zones at each decision step and assigns them relative probabilities based on current demand. We demonstrate the use of D2R2 by integrating it with a Deep Reinforcement Learning multi-agent RS-enabled MoD system in a fleet of 200 vehicle agents serving 10,000 trips extracted from New York taxi trip data. Results show a more fair workload division across the fleet without loss of performance with respect to waiting time and distribution of passengers per vehicle, when compared to baselines with no rebalancing and static pre-defined equiprobable zones.

Demand-Responsive Zone Generation for Real-Time Vehicle Rebalancing in Ride-Sharing fleets

Reinforcement learning is an efficient, widely used machine learning technique that performs well when the state and action spaces are reasonable. This is rarely the case regarding control-related problems, as for instance controlling traffic signals. Here, the state space can be very large. In order to deal with the curse of dimensionality, a rough discretization of such space can be used but this is effective just up to a certain point. A way to mitigate this is to use techniques that generalize the state space such as function approximation. In this paper, a linear function approximation is used. Specifically, SARSA(λ) with  Fourier basis features is implemented to control traffic signals in the agent-based transport simulation MATSim. The results are compared not only to trivial controllers such as fixed-time, but also to state-of-the-art rule-based adaptive methods. It is concluded that SARSA(λ) with Fourier basis features is able to outperform such methods, especially in scenarios with varying traffic demands.

A reinforcement learning approach with Fourier basis linear function approximation for traffic signal control

"* Université Lumière Lyon 2;
roua.elchamaa@univ-lyon2.fr;
yacine.ouzrout@univ-lyon2.fr;
† Saint-Joseph University (ESIB);
rima.kilany@usj.edu.lb;
;
‡ Univ. Lyon, University Claude;
Bernard Lyon 1;
France Email: baudouin.dafflon@univ-lyon1.fr;"

Semantic Traffic Data Analysis for a Local Leader Election Algorithm (LLEA)

Travel apps become more and more popular giving information about the current traffic state to drivers who then adapt their route choice. In commuting scenarios, where people repeatedly travel between a particular origin and destination, learning effects add to this information. In this paper, we analyse the effects on the overall network, if adaptive driver agents share their aggregated experience about route choice in a reinforcement learning (Q-learning) setup. Drivers share what they have learnt about the system, not just information about their current travel times. We can show in a standard scenario that experience sharing can improve convergence times for adaptive driver agents.

Experience Sharing in a Traffic Scenario

"On-Demand Transport (ODT) systems have attracted increasing attention in recent years. 
  Traditional centralized dispatching can achieve optimal solutions with NP-Hard complexity, making it unsuitable for online and dynamic problems. Decentralized approaches can achieve fast feasible solution at run-time with no guarantee on the quality. Starting from feasible not optimal solution, we present in this work a new decentralized, insertion heuristic algorithm to solve a special case of the dynamic Dial-A-Ride-Problem (DARP)  as an ODT system, in which vehicles communicate via Vehicle-to-vehicle communication (V2V)  and take decentralized decisions. In our solution model we add an optimization phase in order to improve the quality  of global solution achieved by decentralized decision at run-time by exchanging resources between vehicles. We evaluate and analyze the promising results of our contributed technique on synthetic data for taxis operating in Saint-Étienne city, against a classical greedy approach."

Decentralized Insertion Heuristic with Runtime Optimization for On-demand Transport Scheduling

"Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search
algorithm that learns a playout policy in order to solve a single
player game. In this paper we apply NRPA to the vehicle routing problem.
This problem is important for large companies that have to manage  
a fleet of vehicles on a daily basis. Real problems are often too large to be solved 
exactly. The algorithm is applied to standard problem of the literature 
and to the specific problems of EDF (Electricité De France, the main french electric utility company). These specific problems have peculiar constraints. 
NRPA gives better result than the algorithm previously used by EDF."

Monte Carlo Vehicle Routing

"When robots undertake tasks in adversarial environments in which they must cooperate with one another (e.g., military applications or the RoboCup Competition), they are at risk for being deceived by competitors. Competi-tors can misdirect the team to gain a positional advantage. Our lab is explor-ing ways in which teams of robots can be misdirected, in part, so counter-deception strategies can be devised. This paper explores how robot shills can be used to misdirect a multi-robot team. It defines behaviors for the agents to be deceived (the mark agents) using the multi-agent coordination literature as well as behaviors for the deceiving team (the shills and lead agent). These behaviors were implemented and simulations were run for a variety of conditions. The results show how shills can facilitate misdirec-tion in certain circumstances. They provide insights into enhancing multi-robot team deception.
"

Wolves in Sheep’s Clothing: Using Shill Agents to Misdirect Multi-Robot Teams

"In 2000, it was predicted that artificially intelligent agents would inevitably
become deceptive. Today, in a world seemingly awash with fake news
and in which we hand over control of our home environments to faux-human
smart devices, it is timely to review the types of deception that have actually
emerged. By reference to examples from diverse branches of AI, we classify research on deception into five novel categories, which we describe according to the human characteristic with which they most closely align: imitating, obfuscating,
tricking, calculating and reframing. We offer this as a way for those within AI
to recognise connections across the discipline and to suggest how AI-instigated
deceptions may be understood by those outside."

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Toward Campus Mail Delivery using BDI

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Competence-Aware Systems for Long-Term Autonomy

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