Han Sung Kim (April 1st 2010). Mechanism Design of Haptic Devices, Advances in Haptics, Mehrdad Hosseini Zadeh, IntechOpen, DOI: 10.5772/8697. 

Tetsuro Saisho (December 18th 2019). Crowdsourcing Strategy of Information Society [Online First], IntechOpen, DOI: 10.5772/intechopen.89665.

Luigi Nasta and Luca Pirolo (February 21st 2019). Crowdsourcing in the Fashion Industry, Toward Super-Creativity - Improving Creativity in Humans, Machines, and Human - Machine Collaborations, Sílvio Manuel Brito, IntechOpen, DOI: 10.5772/intechopen.84607.

New Zealand

We consider a requester who acquires a set of data (e.g. images) that is not owned by one party. In order to collect as many data as possible, crowdsourcing mechanisms have been widely used to seek help from the crowd. However, existing mechanisms rely on third-party platforms, and the workers from these platforms are not necessarily helpful and redundant data are also not properly handled. To combat this problem, we propose a novel crowdsourcing mechanism based on social networks, where the rewards of the workers are calculated by information entropy and a modified Shapley value. This mechanism incentivizes the workers from the network to not only provide all data they have but also further invite their neighbours to offer more data. Eventually, the mechanism is able to acquire all data from all workers on the network and the requester’s cost is no more than the value of the data acquired. The experiments show that our mechanism outperforms traditional crowdsourcing mechanisms.


AAMAS 2020

Collaborative Data Acquisition

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.

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). 

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distribution. We also present a method for offline anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our offline method to discriminate anomalous behaviors in real-world applications are statistically proved.


Optimal Swarm Strategy for Dynamic Target Search and Tracking

In the literature we can find many kinds of modular robot that can build a wide variety of structures. In general, finding an assembly order to reach the final configuration, while respecting the insertion constraints of each kind of modular robot is a difficult process that requires system-specific tuning. 
In this article, we introduce a generic assembly planner by constrained disassembly (GAPCoD) which works with all kinds of modular robots. It outputs a directed acyclic graph where vertices are modules needing to be placed before his child nodes. This graph is obtained through the disassembly of the desired structure submitted to user chosen constraints. We detail the compiler as well as the way to choose constraints and their influence on performance. The robots embed simple path planning algorithm to reach the destination and act as decentralized agents.
Examples are provided to show the possibilities that the compiler offers with two very different robot systems and constraints.

GAPCoD: A Generic Assembly Planner by Constrained Disassembly

Over the course of a day, people often attend many appointments, like meetings or doctor’s appointments, with time and location constraints, as well as perform other tasks like stopping by the grocery store with only location constraints. Optimally scheduling the day’s agenda typically requires a high cognitive load, where people reason about all their constraints at once. An agent that aids a person in scheduling the day’s agenda can potentially reduce the stress associated with scheduling but must be able to 1) perform fast updates and 2) produce new agendas that can be readily understood by the people they are helping. In order to understand how people reason about agenda changes, we first performed a study where participants are asked to perform a series of scheduling tasks and captured their update strategy both subjectively (self-reporting) and objectively (by tracking their reasoning). Our results that show that people use spatial cues and meeting time information to reduce the rescheduling task to a more reasonable size. We then present a novel heuristic for adding tasks to agendas that targets rescheduling to clusters of appointments that are spatio-temporally near the new task. We show that this heuristic approach always finds the optimal solution, while greatly reducing rescheduling time, and performs rescheduling in a way that is similar to our participants’ strategies.

Human-Centered Decision Support for Agenda Scheduling

Previous work has shown the unreliability of existing algorithms in the batch Reinforcement Learning setting, and proposed the theoretically-grounded Safe Policy Improvement with Baseline Bootstrapping (SPIBB) fix: reproduce the baseline policy in the uncertain state-action pairs, in order to control the variance on the trained policy performance. However, in many real-world applications such as dialogue systems, pharmaceutical tests or crop management, data is collected under human supervision and the baseline remains unknown. In this paper, we apply SPIBB algorithms with a baseline estimate built from the data. We formally show safe policy improvement guarantees over the true baseline even without direct access to it. Our empirical experiments on finite and continuous states tasks support the theoretical findings. It shows little loss of performance in comparison with SPIBB when the baseline policy is given, and more importantly, drastically and significantly outperforms competing algorithms both in safe policy improvement, and in average performance.


Safe Policy Improvement with an Estimated Baseline Policy

The computational problem of Influence maximization concerns
the selection of an initial set of nodes in a social network such that,
by sending this set a certain message, its exposure through the
network will be the highest.We propose to study this problem from
a utilitarian point of view. That is, we study a model where there are
two types of messages; one that is more likely to be propagated but
gives a lower utility per user obtaining this message, and another
that is less likely to be propagated but gives a higher utility. In our
model the utility from a user that receives both messages is not
necessarily the sum of the two utilities. The goal is to maximize
the overall utility.
Using an analysis based on bisubmodular functions, we show a
greedy algorithm with a tight approximation ratio of ½. We develop
a dynamic programming based algorithm that is more suitable to
our setting and show through extensive simulations that it outperforms
the greedy algorithm.

Viral Vs. Effective: Utility Based Influence Maximization

We propose a novel technique for Active Malware Analysis (AMA) formalized as a Bayesian game between an analyzer agent and a malware agent, focusing on the decision making strategy for the analyzer. In our model, the analyzer performs an action on the system to trigger the malware into showing a malicious behavior, i.e., by activating its payload. The formalization is built upon the link between malware families and the notion of types in Bayesian games. A key point is the design of the utility function, which reflects the amount of uncertainty on the type of the adversary after the execution of an analyzer action. This allows us to devise an algorithm to play the game with the aim of minimizing the entropy of the analyzer's belief at every stage of the game in a myopic fashion. Empirical evaluation indicates that our approach results in a significant improvement both in terms of learning speed and classification score when compared to other state-of-the-art AMA techniques

Bayesian Active Malware Analysis

First price auctions are widely used in government contracts and ads auctions. In this paper, we consider the Bayesian Nash Equilibrium (BNE) in first price auctions with discrete value distributions. We study the characterization of the BNE in the first price auction and provide an algorithm to compute the BNE at the same time. Moreover, we prove the existence and the uniqueness of the BNE. Some of the previous results in the case of continuous value distributions do not apply to the case of discrete value distributions. In the meanwhile, the uniqueness result in discrete case cannot be implied by the uniqueness property in the continuous case. Unlike
in the continuous case, we do not need to solve ordinary differential equations and thus do not suffer from the solution errors therein. Compared to the method of using continuous distributions to approximate discrete ones, our experiments show that our algorithm is both faster and more accurate."


Bayesian Nash Equilibrium in First-Price Auction with Discrete Value Distributions

Predicting Persuasive Effectivness for Multimodal Behavior Adaptation using Bipolar Weighted Argument Graphs

We study the synthesis of policies for multi-agent systems to implement spatial-temporal tasks. We formalize the problem as a factored Markov decision process subject to so-called graph temporal logic specifications. The transition function and the spatial-temporal task of each agent depend on the agent itself and its neighboring agents. The structure in the model and the specifications enable to develop a distributed algorithm that, given a factored Markov decision process and a graph temporal logic formula, decomposes the synthesis problem into a set of smaller synthesis problems, one for each agent. We prove that the algorithm runs in time linear in the total number of agents. The size of the synthesis problem for each agent is exponential only in the number of neighboring agents,
which is typically much smaller than the number of agents. We demonstrate the algorithm in case studies on disease control and urban security. The numerical examples show that the algorithm can scale to hundreds of agents.
"


Policy Synthesis for Factored MDPs with Graph Temporal Logic Specifications

This work presents the concept of an adaptive safe padding that forces Reinforcement Learning (RL) to synthesise optimal control policies while ensuring safety during the learning process. Policies are synthesised to satisfy a goal, expressed as a temporal logic formula, with maximal probability. Enforcing the RL agent to stay safe during learning might limit the exploration, however we show that the proposed architecture is able to automatically handle the trade-off between efficient progress in exploration (towards goal satisfaction) and ensuring safety. Theoretical guarantees are available on the optimality of the synthesised policies and on the convergence of the learning algorithm. Experimental results are provided to showcase the performance of the proposed method.

Collaborative Data Acquisition

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