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.

State-of-the-art model counters are based on exhaustive DPLL algorithms, and have been successfully used in probabilistic reasoning, one of the key problems in AI. In this article, we present a new exhaustive DPLL algorithm with a formal semantics, a proof of correctness, and a modular design. The modular design is based on the separation of the core model counting algorithm from SAT solving techniques. We also show that the trace of our algorithm belongs to the language of Sentential Decision Diagrams (SDDs), which is a subset of Decision-DNNFs, the trace of existing state-of-the-art model counters. Still, our experimental analysis shows comparable results against state-of-the-art model counters. Furthermore, we obtain the first top-down SDD compiler, and show orders-of-magnitude improvements in SDD construction time against the existing bottom-up SDD compiler.

We formalize the problem of multi-agent path finding with deadlines (MAPF-DL). The objective is to maximize the number of agents that can reach their given goal vertices from their given start vertices within a given deadline, without colliding with each other. We first show that the MAPF-DL problem is NP-hard to solve optimally. We then present an optimal MAPF-DL algorithm based on a reduction of the MAPF-DL problem to a flow problem and a subsequent compact integer linear programming formulation of the resulting reduced abstracted multi-commodity flow network.

Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and the static social learning framework. However, two aspects of dynamics in real-world multiagent scenarios are currently missing in existing works. First, the network topologies can be dynamic where agents may change their connections through rewiring during the course of interactions. Second, the game matrix between each pair of agents may not be static and usually not known as a prior. Both the network dynamic and game uncertainty increase the coordination difficulty among agents. In this paper, we consider a multiagent dynamic social learning environment in which each agent can choose to rewire potential partners and interact with randomly chosen neighbors in each round. We propose an optimal rewiring strategy for agents to select most beneficial peers to interact with for the purpose of maximizing the accumulated payoff in repeated interactions. We empirically demonstrate the effectiveness and robustness of our approach through comparing with benchmark strategies. The performance of three representative learning strategies under our social learning framework with our optimal rewiring is investigated as well.

First, the project attempts to utilize game theory to "ascertain the intent of the adversary." Interestingly, the project assumes that all agents are rational--meaning, I believe, that agents will act to maximize their respective utilities--and that normative theories about how agents will act are important. Explicitly excluded, however, are "descriptive theories that focus on … intangible aspects such as human judgment, irrationality, biases, [and] cognitive limitations." While this may make sense from a streamlined game-theoretic approach, it in no way reflects the real world. Indeed, even game theorists acknowledge the importance of factors that COMPASS excludes, which is why they make ample use of theories of bounded rationality, uncertainty, imperfect information, and the very notion of "adversarial."

Harald Sack is Professor for Information Services Engineering at two of the most renowned research institutions in Europe: FIZ Karlsruhe and AIFB. He is a part of SEMANTiCS' research and innovation track program committee as well as of the conference's permanent advisory board. His publications include more than 130 papers in international journals and conferences and three standard textbooks on networking technologies. In this interview he speaks about the limited capabilities of search engines, the necessity of data being open and the coffee culture in Vienna. You have been working in many research areas such as semantic web technologies, knowledge representations, multimedia analysis & retrieval.