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Rommel Carvalho, Kathryn Laskey, Paulo Costa, Marcelo Ladeira, Laecio Santos and Shou Matsumoto (January 1st 2010). UnBBayes: Modeling Uncertainty for Plausible Reasoning in the Semantic Web, Semantic Web, Gang Wu, IntechOpen, DOI: 10.5772/7315. 

Andr&eacute; A. Keller (October 1st 2009). Optimal Economic Stabilization Policy under Uncertainty, Advanced Technologies, Kankesu Jayanthakumaran, IntechOpen, DOI: 10.5772/8222. 

Kenjiro Yanagi (April 28th 2020). Uncertainty Relations [Online First], IntechOpen, DOI: 10.5772/intechopen.92137.

New Zealand

The problem of multi-agent resource allocation is important and well-studied within AI and economics. The general assumption is that the amount of each resource is known beforehand. However, many real-world problems, the exact amount of each resource may not be known at the time of decision making, e,g. in the case of weather dependent renewable energy production. This work considers a homogeneous divisible resource where the available amount is given by a probability distribution. In general, a model for efficient usage under fairness and the possibilities of manipulation is studied. Firstly, the notion of ex-ante envy-freeness, where, in expectation, agents weakly prefer their allocation over every other agent&#39;s allocation is introduced. For this case the tension between fairness and social welfare is considered. The price of envy-freeness is at least $\Omega(n)$, where $n$ is the number of agents and the problem of optimising ex-ante social welfare subject to ex-ante envy-freeness is strongly NP-hard. Additionally, the possibility for an integer program to calculate the optimal ex-ante envy-free allocation for linear satiable valuation functions is presented.


AAMAS 2020

Efficiency and Fairness of Resource Utilisation under Uncertainty

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). 

The problem of multi-agent resource allocation is important and well-studied within AI and economics. The general assumption is that the amount of each resource is known beforehand. However, many real-world problems, the exact amount of each resource may not be known at the time of decision making, e,g. in the case of weather dependent renewable energy production. This work considers a homogeneous divisible resource where the available amount is given by a probability distribution. In general, a model for efficient usage under fairness and the possibilities of manipulation is studied. Firstly, the notion of ex-ante envy-freeness, where, in expectation, agents weakly prefer their allocation over every other agent's allocation is introduced. For this case the tension between fairness and social welfare is considered. The price of envy-freeness is at least $\Omega(n)$, where $n$ is the number of agents and the problem of optimising ex-ante social welfare subject to ex-ante envy-freeness is strongly NP-hard. Additionally, the possibility for an integer program to calculate the optimal ex-ante envy-free allocation for linear satiable valuation functions is presented.


technical paper

Cost Allocation in Ridesharing

We formalize an allocation model that is more gen- eral than the well-studied Shapley-Scarf housing market. In our model, the agents do not just care which house or resource they get but also care about who gets their own resource. This assump- tion is especially important when considering tem- porary exchanges in which each resource is eventu- ally returned to the owner. We show that several positive axiomatic and computational results that hold for housing markets do not extend to the more general setting. We then identify natural restric- tions on the preferences of agents for which several positive results do hold. One of our central results is a general class of algorithms that return any allo- cation that is individually rational and Pareto opti- mal with respect to the responsive set extension.


The Temporary Exchange Problem

One of the challenging problems of impartial combinatorial games (ICGs) is to construct generalized winning strategies for possibly infinitely many states. In this paper, we investigate synthesizing generalized winning strategies for ICGs. To this end, we first propose a logical framework to formalize ICGs based on linear integer arithmetic. We then propose an approach to generating the winning strategy for ICGs. Experimental results on several games demonstrate that our approach is effective in most of these games.


Automatic Synthesis of Generalized Winning Strategy of Impartial Combinatorial Games

In many tasks, classifiers play a fundamental role in the way an
agent behaves. Most rational agents collect sensor data from the environment,
classify it, and act based on that classification. Recently,
deep neural networks (DNNs) have become the dominant approach
to develop classifiers due to their excellent performance. When
training and evaluating the performance of DNNs, it is normally
assumed that the cost of all misclassification errors are equal. However,
this is unlikely to be true in practice. Incorrect classification
predictions can cause an agent to take inappropriate actions. The
costs of these actions can be asymmetric, vary from agent-to-agent,
and depend on context. In this paper, we discuss the importance of
considering risk and uncertainty quantification together to reduce
agents’ cost of making misclassifications using deep classifiers.

Not all Mistakes are Equal

Existing Deep Reinforcement Learning (DRL) methods are still faced with sample inefficiency, so it is difficult to learn from scratch. Transfer Learning (TL) has shown great potential to accelerate DRL by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. We propose a novel Policy Transfer Framework (PTF) based on this idea. Our PTF models multi-policy transfer as option learning, and we directly optimize the target policy using the transferred knowledge. We propose an adaptive, heuristic mechanism to avoid negative transfer. PTF can be easily combined with DRL methods. And experimental results show it outperforms existing DRL approaches, and state-of-the-art policy transfer methods both in discrete and continuous action space.


Efficient Deep Reinforcement Learning through Policy Transfer

Social structures naturally arise from social networks, yet no model well interprets the emergence of structural properties in a unified dimension. Here, we unify explanations for the emergence of net- work structures by revealing the pivotal role of social capital, i.e., benefits that a society grants to individuals, in network formation. We propose a game-based framework social capital games that math- ematically conceptualizes social capital. Through this framework, individuals are regarded as independent learning agents that aim to gain social capital via building interpersonal ties. We adopt multi- agent reinforcement learning (MARL) to train agents. By varying configurations of the game, we observe the emergence of classical structures of community, small-world, and core-periphery.

Social Structure Emergence: A multi-agent Reinforcement Learning Framework for Relationship Building

Realistic models of decision-making and social interactions, considering the nature of memory and biases, continue to be an area of immense interest. Emotion and mood are a couple of key factors that play a major role in decisions, nature of social interactions, size of the social network, and the level of engagement. Most of the prior work in this direction focused on a single trait, behavior, or bias. However, this work builds an integrated model that considers multiple traits such as loneliness, the drive to interact, the memory, and mood biases in an agent. The agent system comprises of rational, manic, depressed, and bipolar agents. The system is modeled with an interconnected network, and the size of the personal network of each agent is based on its nature. We consider a game of iterated interactions where an agent cooperates based on its past experiences with the other agent. Through simulation, the effects of various biases and comparative performances of agent types is analyzed. Taking the performance of rational agents as the baseline, manic agents do much better, and depressed agents do much worse. The payoffs also exhibit an almost-linear relationship with the extent of mania.


Analyzing the Effects of Memory Biases and Mood Disorders on Social Performance

The study of stable fractional matchings is fairly recent and moreover, is scarce. This paper reports the first investigation into the important but unexplored topic of incentive compatibility of matching mechanisms to find stable fractional matchings.We focus our attention on matching instances under strict preferences. First, we make the significant observation that there are matching instances for which no mechanism that produces a stable fractional matching is incentive compatible.We then characterize restricted settings of matching instances admitting unique stable fractional matchings. For this class of instances, we prove that every mechanism that produces the unique stable fractional matching is (a) incentive compatible and (b) resistant to coalitional manipulations. We provide a polynomial-time algorithm to compute the stable fractional matching as well.

A Study of Incentive Compatibility and Stability Issues in Fractional Matchings

"The theme park problem is a research framework that evaluates measures for improving the satisfaction of visitors to crowded amusement parks on a multi-agent simulation (MAS).
To make the MAS more realistic, we propose the followings:
1) visitor surplus, which evaluates visitors' satisfaction based on microeconomics,
2) multinomial linear model, a selection behavior model based on visitor surplus,
and 3) a tolerance limit model, which estimates the distribution of the visitors' tolerance limits of waiting times by analyzing questionnaire results."


Theme Park Simulation based on Questionnaires for Maximizing Visitor Surplus

We present a method that analyzes a person's negotiation behavior to automatically detect co-occurrence of tactics and combination of tactics (i.e., negotiation styles). We first identify action features consistent with use of the common negotiation tactics based on prior research in negotiation. Next, we apply regularized linear regression over a negotiation dataset to assess how effective particular tactics are in predicting the negotiation outcome. Finally, we use a supervised variant of a topic model to derive effective negotiation styles. Results from the clusters produced by the topic models provide insights regarding the effectiveness of negotiation styles that people utilize.


Efficiency and Fairness of Resource Utilisation under Uncertainty

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Cost Allocation in Ridesharing

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Cost Allocation in Ridesharing

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

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