United Kingdom

Traffic congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past research has shown that in small scale mixed traffic scenarios with both AVs and human-driven vehicles, a small fraction of AVs executing a controlled multiagent driving policy can mitigate congestion. In this paper, we scale up existing approaches and develop new multiagent driving policies for AVs in scenarios with greater complexity. We start by showing that a congestion metric used by past research is manipulable in open road network scenarios where vehicles dynamically join and leave the road. We then propose using a different metric that is robust to manipulation and reflects open network traffic efficiency. Next, we propose a modular transfer learning approach and use it to scale up the multiagent driving policy to a realistic simulated scenario that is an order of magnitude larger than past scenarios (hundreds rather than tens of vehicles). Our experimental study shows that the resulting policy improves traffic efficiency over human-driven traffic in a large open network, where existing approaches fail to do so. Another key advantage of our modular transfer learning approach is that it avoids collecting samples from entire network, which saves up to 80% of training and data collection time in our experiments. Finally, we show for the first time a distributed multiagent policy that improves congestion over human-driven traffic. The distributed approach is more realistic as it does not require adding communication infrastructure, it relies on existing sensing capabilities, and its complexity scales linearly with the number of vehicles.

AAMAS 2021

Scalable Multiagent Driving Policies for Reducing Traffic Congestion

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

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Automatic gesture generation from speech generally relies on implicit modelling of the nondeterministic speech-gesture relationship and can result in averaged motion lacking defined form. Here, we propose a database-driven approach of selecting gestures based on specific motion characteristics that have been shown to be associated with the speech audio. We extend previous work that identified expressive parameters of gesture motion that can both be predicted from speech and are perceptually important for a good speech-gesture match, such as gesture velocity and finger extension. A perceptual study was performed to evaluate the appropriateness of the gestures selected with our method. We compare our method with two baseline selection methods. The first respects timing, the desired onset and duration of a gesture, but does not match gesture form in other ways. The second baseline additionally disregards the original gesture timing for selecting gestures. The gesture sequences from our method were rated as a significantly better match to the speech than gestures selected by either baseline method.

It's A Match! Gesture Generation Using Expressive Parameter Matching

Asymmetric distributed constraint optimization problems (ADCOPs) in which agents are partially cooperative, is a model for representing multi-agent optimization problems  in which agents, are willing to cooperate in order to achieve a global goal,  as long as some minimal threshold on their personal utility is satisfied.

Partially Cooperative Multi-Agent Periodic Indivisible Resource Allocation

Van Zuylen et al. introduced the notion of a popular ranking in a voting context, where each voter submits a strictly-ordered list of all candidates. A popular ranking pi of the candidates is at least as good as any other ranking sigma in the following sense: if we compare pi to sigma, at least half of all voters will always weakly prefer pi. Whether a voter prefers one ranking to another is calculated based on the Kendall distance.

On Weakly and Strongly Popular Rankings

Relational agents who personalise their message to individual users need to learn about the user first. In the context of an agent who personalises the inclusion of 10 relational cues in its dialogue, we explore training the agent based on first time user’s responses to a single example of each cue prior to the session with the virtual advisor. We designed a between subjects study with three groups: Empathic (all relational cues); Neutral (no relational cues) and Adaptive (only helpful cues included). We found that in the Adaptive group, students received what they found helpful more often than in the other two groups. Analysis of the discrepancy between what user’s found helpful and what they received was least in the Adaptive group and greatest in the Neutral group.

Personalising the Dialogue of Relational Agents for First-Time Users

The prevalence of e-commerce applications poses new trust challenges that render traditional Trust and Reputation Management (TRM) approaches inadequate. The first challenge is that TRM is built on evidence (direct or indirect observations) but evidence is becoming increasingly sparse because nowadays users have many more venues to share information. This makes it hard to derive trust models that are robust to attacks such as whitewashing and Sybil attacks. Second, the cost of attacks has reduced significantly due to the widespread presence of bots in e-commerce applications, which tends to invalidate the traditional assumption that majority users are honest.

A Blockchain-Enabled Quantitative Approach to Trust and Reputation Management with Sparse Evidence

Individuals in a social network may form their views as a result of the influence exerted by their connections. In elections, for example, while they might initially support one candidate, social influence may lead them to support another. Here, we investigate whether a recently proposed metric, influence gap, designed to measure the effect of social influence in voting on social networks, is able to predict the outcome of a vote on networks exhibiting community structure, i.e., made of highly interconnected components, and therefore more resembling of real-world interaction. To encode communities, we extend the classical model of caveman graphs to a richer graph family that displays levels of homophily, i.e., where connections and opinions are highly intertwined. We show that, across these graphs, there are important cases when the influence gap correlation is a weak predictor due to communities, and a simpler metric, counting the initial partisan majority, provides a more accurate prediction overall. Using regression models, we further demonstrate that the influence gap combined with the more successful metrics does increase their predictive power for some levels of homophily.

Predicting Voting Outcomes in Presence of Communities

The Possible Winner (PW) problem, a fundamental algorithmic problem in computational social choice, concerns elections where voters express only partial preferences between candidates. A sequence of investigations led to a complete classification of the complexity of this problem for all pure positional scoring rules: the PW problem is in P for the plurality and veto rules, and NP-complete for all other such rules. The PW problem has also been studied on classes of restricted partial orders, such as partitioned partial orders and truncated partial orders; one of the findings is that there are positional scoring rules for which the complexity of the PW problem drops from NP-complete to P on such restricted partial orders. Here, we investigate the PW problem on partial chains, i.e., partial orders that consist of a total order on a subset of their domains. Such orders arise naturally in a variety of settings, including rankings of movies or restaurants. We classify the complexity of the PW problem on partial chains by establishing that, perhaps surprisingly, this restriction does not change the complexity of the problem. Specifically, we show that the PW problem on partial chains is NP-complete for all pure positional scoring rules other than the plurality rule and the veto rule, while, of course, for the latter two rules this problem remains in P.

Classifying the Complexity of the Possible Winner Problem on Partial Chains

We study settings where a set of identical, reusable resources must be allocated in an online fashion to arriving agents. Each arriving agent is patient and willing to wait for some period of time to be matched. When matched, each agent occupies a resource for a certain amount of time, and then releases it, gaining some utility from having done so. The goal of the system designer is to maximize overall utility given some prior knowledge of the distribution of arriving agents. We are particularly interested in settings where demand for the resources far outstrips supply, as is typical in the provision of social services, for example homelessness resources. We formulate this problem as online bipartite matching with reusable resources and patient agents. We develop new, efficient nonmyopic algorithms for this class of problems, and compare their performance with that of greedy algorithms in a variety of simulated settings, as well as in a setting calibrated to real-world data on household demand for homelessness services. We find substantial overall welfare benefits to using our nonmyopic algorithms, particularly in more extreme settings -- those where agents are unwilling or unable to wait for resources, and where the ratio of resource demand to supply is particularly high.

Efficient Nonmyopic Online Allocation of Scarce Reusable Resources

We introduce the idea of a finite sequence of coalition formation games over a set of agents, and we call it Sequential Characteristic-Function Game (SCFG). We define the solution of such a game as a corresponding sequence of coalition structures that must be related by a given feasibility relation, so no coalition structure can be evaluated in isolation. A sequence satisfying this condition is called Feasible Coalition-Structure Sequence (FCSS). Such games can be a useful abstraction for modelling various scenarios, in particular those for real-world disaster management that we consider in this paper. We give an algorithm for computing an FCSS and evaluate it experimentally. Our results show that an SCFG can represent various classical variations of characteristic-function games, and our algorithm solves instances with a reasonable number of agents.

Feasible Coalition Sequences

In this paper, we propose and study the cascade submodular maximization problem under the adaptive setting. The input of our problem is a set of items, each item is in a particular state (i.e., the marginal contribution of an item) which is drawn from a known probability distribution. However, we can not know its actual state before selecting it. As compared with existing studies on stochastic submodular maximization, one unique setting of our problem is that each item is associated with a continuation probability which represents the probability that one is allowed to continue to select the next item after selecting the current one. Intuitively, this term captures the externality of selecting one item to all its subsequent items in terms of the opportunity of being selected.  Therefore, the actual set of items that can be selected by a policy depends on the specific ordering it adopts to select items, this makes our problem fundamentally different from classical submodular set optimization problems. Our objective is to identify the best sequence of selecting items so as to maximize the expected utility of the selected items. We propose a class of stochastic utility functions, adaptive cascade submodular functions, and show that the objective functions in many practical application domains satisfy adaptive cascade submodularity. Then we develop a $0.12$ approximation algorithm to the adaptive cascade submodular maximization problem.

Scalable Multiagent Driving Policies for Reducing Traffic Congestion

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It's A Match! Gesture Generation Using Expressive Parameter Matching

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Scalable Multiagent Driving Policies for Reducing Traffic Congestion

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Next from AAMAS 2021

It's A Match! Gesture Generation Using Expressive Parameter Matching

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Fault-Tolerant Offline Multi-Agent Path Planning

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