We examine the effects of inter-agent variation on the ability of a decentralized multi-agent system (MAS) to self-organize in response to dynamically changing task demands. In decentralized biological systems, inter-agent variation as minor as noise has been observed to improve a system's ability to redistribute agent resources in response to external stimuli. We compare the performance of two MAS consisting of agents with and without noisy sensors on a cooperative tracking problem and examine the effects of inter-agent variation on agent behaviors and how those behaviors affect system performance. Results show that small variations in how individual agents respond to stimuli can lead to more accurate and stable allocation of agent resources.

In recent years, we have witnessed a blossoming of research proposals addressing thechallenges in reasoning about action and change in domains that include an agent operatingin a multi-agent setting. In particular, the recent emphasis has been on dealing with domains that involve agents reasoning not only about the state of the world but also about the knowledge andbeliefs of other agents. An open challenge is the management of conflicting and incorrectbeliefs. This paper seeks to introduce a solution to this through the use of doxastic attitudes. Built on top of the action language mA+, we extend the transition functions of an agent to include this idea of attitudes and showcase how these work in two different examples.

We address a problem of area protection in graph-based scenarios with multiple mobile agents where connectivity is maintained among agents to ensure they can communicate. The problem consists of two adversarial teams of agents that move in an undirected graph shared by both teams. Agents are placed in vertices of the graph; at most one agent can occupy a vertex; and they can move into adjacent vertices in a conflict free way. Teams have asymmetric goals: the aim of one team - attackers - is to invade into given area while the aim of the opponent team - defenders - is to protect the area from being entered by attackers by occupying selected vertices. The team of defenders need to maintain connectivity of vertices occupied by its own agents in a visibility graph. The visibility graph models possibility of communication between pairs of vertices. We study strategies for allocating vertices to be occupied by the team of defenders to block attacking agents where connectivity is maintained at the same time. To do this we reserve a subset of defending agents that do not try to block the attackers but instead are placed to support connectivity of the team. The performance of strategies is tested in multiple benchmarks. The success of a strategy is heavily dependent on the type of the instance, and so one of the contributions of this work is that we identify suitable strategies for diverse instance types.

We use an algebraic viewpoint, namely a matrix framework to deal with the problem of resource allocation under uncertainty in the context of a qualitative approach. Our basic qualitative data are a plausibility relation over the resources, a hierarchical relation over the agents and of course the preference that the agents have over the resources. With this data we propose a qualitative binary relation $\unrhd$ between allocations such that $\mathcal{F}\unrhd \mathcal{G}$ has the following intended meaning: the allocation $\mathcal{F}$ produces more or equal social welfare than the allocation $\mathcal{G}$. We prove that there is a family of allocations which are maximal with respect to $\unrhd$. We prove also that there is a notion of simple deal such that optimal allocations can be reached by sequences of simple deals. Finally, we introduce some mechanism for discriminating {optimal} allocations.

We present a simple model of a non-equilibrium self-organizing market where asset prices are partially driven by investment decisions of a bounded-rational agent. The agent acts in a stochastic market environment driven by various exogenous "alpha" signals, agent's own actions (via market impact), and noise. Unlike traditional agent-based models, our agent aggregates all traders in the market, rather than being a representative agent. Therefore, it can be identified with a bounded-rational component of the market itself, providing a particular implementation of an Invisible Hand market mechanism. In such setting, market dynamics are modeled as a fictitious self-play of such bounded-rational market-agent in its adversarial stochastic environment. As rewards obtained by such self-playing market agent are not observed from market data, we formulate and solve a simple model of such market dynamics based on a neuroscience-inspired Bounded Rational Information Theoretic Inverse Reinforcement Learning (BRIT-IRL). This results in effective asset price dynamics with a non-linear mean reversion - which in our model is generated dynamically, rather than being postulated. We argue that our model can be used in a similar way to the Black-Litterman model. In particular, it represents, in a simple modeling framework, market views of common predictive signals, market impacts and implied optimal dynamic portfolio allocations, and can be used to assess values of private signals. Moreover, it allows one to quantify a "market-implied" optimal investment strategy, along with a measure of market rationality. Our approach is numerically light, and can be implemented using standard off-the-shelf software such as TensorFlow.

International travelers could soon be greeted by AI powered lie-detecting robot kiosks before crossing borders. The system, known as the Automated Virtual Agent for Truth Assessment in Real Time, was tested at the U.S.-Mexico border on travelers deemed a low risk six years ago. Since then, it has been tested at the Canadian Border Services Agency and the European Union, and it is hoped this can soon help agents screen for criminals and even potential terrorists. The system, known as the Automated Virtual Agent for Truth Assessment in Real Time, has been tested by Canada, the U.S., and the European Union and it's hoped this can soon help agents screen for criminals and even potential terrorists The robot uses eye-detection software along with an array of sensors to pick up on the physiological signs that indicate a person is lying, and once it becomes suspicious, it can flag the passenger for further inspection. Donald Trump requested $223 million from Homeland Security for 2019 for'high-priority infrastructure, border security technology improvements,' as in addition to $210.5 million for hiring new border agents.

"We live in a network world. Everything we do is an outcome of multiple elements. The pervasion of social media in our lives means hundreds and thousands of tweets and retweets by the minute. Gone are the times when information asymmetry was exploited," remarked Dr Alok Chaturvedi, professor of Management and Computer Science, Purdue University while initiating a talk at ORF Delhi on Big Data and Agent Based Simulation for Policy Analysis on 8 May, 2018. The discussion was moderated by Rakesh Sood, Distinguished Fellow, ORF and a former ambassador.

With the aid of an intelligent system, a group of people (the key group) faces a hiring decision about many candidates who are characterized by attributes, such as experiences, technical skills, communication skills, etc. The goal is to help the key group make a group decision without directly eliciting their full preferences over all candidates, which is often infeasible given the vast number of candidates. Instead, the intelligent system may ask fellow employees (the regular group) about their preferences in order to learn about the key group's preferences. How can the intelligent system decide which member in the regular group to ask and which question should be asked? This example illustrates the preference elicitation problem, which has been widely studied in the field of recommender systems [Loepp et al., 2014], healthcare [Erdem and Campbell, 2017, Weernink et al., 2014], marketing [Huang and Luo, 2016], stable matching [Drummond and Boutilier, 2014, Rastegari et al., 2016], etc. Most previous works studied a special case of the aforementioned scenario, in which the regular group is the key group. The objective of preference elicitation is to achieve some goal using as few samples (data) as possible. A common approach is to adaptively ask questions that maximize expected information gain, measured by some information criteria. Moreover, most previous work focused on a specific type of elicitation questions, e.g.

Many multi-agent systems (MASs) are situated in stochastic environments. Some such systems that are based on the partially observable Markov decision process (POMDP) do not take the benevolence of other agents for granted. We propose a new POMDP-based framework which is general enough for the specification of a variety of stochastic MAS domains involving the impact of agents on each other's reputations. A unique feature of this framework is that actions are specified as either undirected (regular) or directed (towards a particular agent), and a new directed transition function is provided for modeling the effects of reputation in interactions. Assuming that an agent must maintain a good enough reputation to survive in the network, a planning algorithm is developed for an agent to select optimal actions in stochastic MASs. Preliminary evaluation is provided via an example specification and by determining the algorithm's complexity.

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning. Our intention is to put intelligent agents into a simulated natural context and verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population. To achieve this, we simulate a large-scale predator-prey world, where the laws of the world are designed by only the findings or logical equivalence that have been discovered in nature. We endow the agents with the intelligence based on deep reinforcement learning (DRL). In order to scale the population size up to millions agents, a large-scale DRL training platform with redesigned experience buffer is proposed. Our results show that the population dynamics of AI agents, driven only by each agent's individual self-interest, reveals an ordered pattern that is similar to the Lotka-Volterra model studied in population biology. We further discover the emergent behaviors of collective adaptations in studying how the agents' grouping behaviors will change with the environmental resources. Both of the two findings could be explained by the self-organization theory in nature.