Wenwen Tu (October 29th 2019). Deep Learning Based Prediction of Transfer Probability of Shared Bikes Data, Intelligent System and Computing, Yang (Cindy) Yi, IntechOpen, DOI: 10.5772/intechopen.89835. 

Pavol Navrat, Anna Bou Ezzeddine, Lucia Jastrzembska and Tomas Jelinek (March 1st 2010). Exploring the Bee Hive Metaphor as a Model for Problem Solving: Search, Optimisation, and More, Web Intelligence and Intelligent Agents, Zeeshan-Ul-Hassan Usmani, IntechOpen, DOI: 10.5772/8376. 

Cristina De Castro and Paolo Toppan (October 1st 2009). Customisation of Learning Paths and Network Optimisation in E-learning Systems, Technology, Education and Development, Aleksandar Lazinica and Carlos Calafate, IntechOpen, DOI: 10.5772/7292.

New Zealand

&quot;Peer-to-peer ridesharing (P2P-RS) enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic), as we showed in a recent publication where we proposed an online approximate solution algorithm for large-scale P2P-RS.
In this paper we tackle the fundamental question of assessing the benefit of predicting ridesharing requests in the context of P2P-RS optimisation. 
Results on a public real-world show that, by employing a perfect predictor, the total reward can be improved by 5.27% with a forecast horizon of $1$ minute.
On the other hand, a vanilla long short-term memory neural network cannot improve upon a baseline predictor that simply replicates the previous day&#39;s requests, whilst achieving an almost-double accuracy&quot;

AAMAS 2020

Predicting Requests in Large-Scale Online P2P Ridesharing

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. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


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.

"Peer-to-peer ridesharing (P2P-RS) enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic), as we showed in a recent publication where we proposed an online approximate solution algorithm for large-scale P2P-RS.
In this paper we tackle the fundamental question of assessing the benefit of predicting ridesharing requests in the context of P2P-RS optimisation. 
Results on a public real-world show that, by employing a perfect predictor, the total reward can be improved by 5.27% with a forecast horizon of $1$ minute.
On the other hand, a vanilla long short-term memory neural network cannot improve upon a baseline predictor that simply replicates the previous day's requests, whilst achieving an almost-double accuracy"

technical paper

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 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). 

Optimal Swarm Strategy for Dynamic Target Search and Tracking

"We present VerSecTis -- a new experimental tool for the verification of timed security protocols' (TSP) modelled by Timed Interpreted Systems (TIS).
In addition to the TSP's time-independent properties, our tool can also examine the time dependencies of the TSP's executions on which their security depends.
The verification method consists of a new TSPs' modelling method and
a translation of the reachability problem for TIS to the Satisfiability Modulo Theories problem. We also deliver nineteen TSPs to verify, and we plan to expand the tool with further protocols."

VerSecTis - An Agent Based Model Checker for Security Protocols (Demo)

**Please click on the button bellow to see this lecture on SlidesLive:**

[![](https://assets.underline.io/uploads/markdown_image/1/image/08e10ad349922f32e7322b77b8df9019.png)](https://slideslive.com/38946802/boston-dynamics)

**Abstract:**

Hurricanes are devastating natural disasters. In deciding how to respond to a hurricane, in particular whether and when to evacuate, a decision-maker must weigh often highly uncertain and contradictory information about the future path and intensity of the storm. To effectively plan to help people during a hurricane, it is crucial to be able to predict and understand this evacuation decision. To this end, we propose a computational model of human sequential decision-making in response to a hurricane based on a Partial Observable Markov Decision Process (POMDP) that models concerns, uncertain beliefs about the hurricane, and future information. We evaluate the model in two ways. First, hurricane data from 2018 was used to evaluate the model's predictive ability on real data. Second, a simulation study was conducted to qualitatively evaluate the sequential aspect of the model. The evaluation with hurricane data shows that our proposed features are significant predictors and they can predict the data well, within and across distinct hurricane datasets. The simulation results show that, across different setups, our model generates predictions on the sequential decisions making aspect that align with expectations qualitatively and suggests the importance of modeling information.

A Computational Model of Human Hurricane Evacuation Decision

Motivated by the apparent societal need to design complex autonomous systems whose decisions and actions are humanly intelligible, the study of explainable artificial intelligence, and with it, research on explainable autonomous agents has gained increased attention from the research community. One important objective of research on explainable agents is the evaluation of explanation approaches in human-computer interaction studies. In this demonstration paper, we present a way to facilitate such studies by implementing explainable agents and multi-agent systems that i) can be deployed as static files, not requiring the execution of server-side code, which minimizes administration and operation overhead, and ii) can be embedded into web front ends and other JavaScript-enabled user interfaces, hence increasing the ability to reach a broad range of users. We then demonstrate the approach with the help of an application that was designed to assess the effect of different explainability approaches on the human intelligibility of an unmanned aerial vehicle simulation.

Explainable Agents as Static Web Pages: UAV Simulation Example

We propose a novel method for analyzing and visualizing the complexity of standard reinforcement learning (RL) benchmarks based on score distributions. A large number of policy networks are generated by randomly guessing their parameters, and then evaluated on the benchmark task; the study of their aggregated results provide insights into the benchmark complexity. Our method guarantees objectivity of evaluation by sidestepping learning altogether: the policy network parameters are generated using Random Weight Guessing (RWG), making our method agnostic to (i) the classic RL setup, (ii) any learning algorithm, and (iii) hyperparameter tuning. We show that this approach isolates the environment complexity, highlights specific types of challenges, and provides a proper foundation for the statistical analysis of the task's difficulty. We test our approach on a variety of classic control benchmarks from the OpenAI Gym, where we show that small untrained networks can provide a robust baseline for a variety of tasks. The networks generated often show good performance even without gradual learning, incidentally highlighting the triviality of a few popular benchmarks.

Analyzing Reinforcement Learning Benchmarks using Random Weight Guessing

Multiagent systems can use commitments as the core of a general coordination infrastructure, supporting both cooperative and non-cooperative interactions. For agents whose objectives are aligned, and where one agent can help another achieve greater reward by sacrificing some of its own reward, their cooperative commitment should be chosen to maximize their joint reward. We present a solution to the problem of how cooperative agents can efficiently find an (approximately) optimal commitment by querying about carefully selected commitment choices. We prove structural properties of the agents' values as functions of the parameters of the commitment specification, and develop a greedy method for composing a query with provable approximation bounds, that we empirically show can find nearly optimal commitments in a fraction of the time that methods that lack our insights require.

Efficient Querying for Cooperative Commitments

In scenarios that require teamwork, we usually have at hand a variety of specific tasks, for which we need to form a team in order to carry out each one. Here we target the problem of matching teams with tasks within the context of education, and specifically in the context of forming teams of students and allocating them to internship programs. We provide a formalization of the problem, and show the computational hardness of solving it optimally.
Thereafter, we propose a heuristic anytime algorithm that generates an initial team allocation which later on attempts to improve in an iterative process.
Moreover, we present the results of our evaluation, and compare the results with a state-of-the-art optimizer.

TAIP: an anytime algorithm for allocating student teams to internship programs

Theory of Mind is commonly defined as the ability to attribute mental states (e.g., beliefs, goals) to oneself, and to others. A large body of previous work---from the social sciences to artificial intelligence---has observed that Theory of Mind capabilities are central to providing an explanation to another agent or when explaining that agent’s behaviour. In this paper, we build and expand upon previous work by providing an account of explanation in terms of the beliefs of agents and the mechanism by which agents revise their beliefs given possible explanations. Our account satisfies a number of important desiderata which we posit must be met by accounts of explanation that purport to employ Theory of Mind, in contrast to previous work which has addressed some but not all of these desiderata.

Towards the Role of Theory of Mind in Explanation

Maximizing Plan Legibility in Stochastic Environments (Extended Abstract)

In this work, the Distributed Bayesian (D-Bay) algorithm is applied to an autonomous search use case. Within the use case multiple unmanned aerial vehicles equipped with cameras cooperatively search an area and minimize the required time. The use case is mod- eled within the continuous Distributed Constraint Optimization Problem (DCOP) framework. This framework extends the (discrete) DCOP framework by allowing variables with continuous domains. Compared to similar DCOP solvers, the characteristics of the D- Bay algorithm are well suited for the use case and allow for the implementation on autonomous vehicles with limited resources (computational power, memory, and communication bandwidth). Experimental results are given and these results are used to vali- date a simulation environment. Within the simulation environment various scenarios are implemented. The D-Bay algorithm was able to find solutions within 3.5 % of the optimal solution with a limited amount of samples per agent.

Predicting Requests in Large-Scale Online P2P Ridesharing

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Optimal Swarm Strategy for Dynamic Target Search and Tracking

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Predicting Requests in Large-Scale Online P2P Ridesharing

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Next from AAMAS 2020

Optimal Swarm Strategy for Dynamic Target Search and Tracking

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Simultaneously Updating All Persistence Values in Reinforcement Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads