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A distributed joint-learning and auction algorithm for target assignment

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Research output : Chapter in Book/Report/Conference proceeding › Conference contribution

We consider an agent-target assignment problem in an unknown environment modeled as an undirected graph. Agents incur cost or reward while traveling on the edges of this graph. Agents do not know the graph or the locations of the targets on it. However, they can obtain local information about these by local sensing and communicating with other agents within a limited range. To solve this problem, we come up with a new distributed algorithm that integrates Q-Learning and a distributed auction. The Q-Learning part helps estimate the assignment benefits calculated by summing up rewards over the graph edges for each agent-target pair, while the auction part takes care of assigning agents to targets in a distributed fashion. The algorithm is shown to terminate with a near-optimal assignment in a finite time. Optimality refers to the assignment benefit maximization, which can depend on a target-agent pair value, and the routing cost of the agent to visit the target.

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  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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  • 10.1109/CDC.2010.5718180

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  • Link to publication in Scopus
  • Link to the citations in Scopus

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  • Auctions Mathematics 100%
  • Learning Algorithm Mathematics 85%
  • Assignment Mathematics 65%
  • Target Mathematics 61%
  • Learning algorithms Engineering & Materials Science 59%
  • Q-learning Mathematics 30%
  • Reward Mathematics 20%
  • Costs Mathematics 13%

T1 - A distributed joint-learning and auction algorithm for target assignment

AU - Sadikhov, Teymur

AU - Zhu, Minghui

AU - Martínez, Sonia

N2 - We consider an agent-target assignment problem in an unknown environment modeled as an undirected graph. Agents incur cost or reward while traveling on the edges of this graph. Agents do not know the graph or the locations of the targets on it. However, they can obtain local information about these by local sensing and communicating with other agents within a limited range. To solve this problem, we come up with a new distributed algorithm that integrates Q-Learning and a distributed auction. The Q-Learning part helps estimate the assignment benefits calculated by summing up rewards over the graph edges for each agent-target pair, while the auction part takes care of assigning agents to targets in a distributed fashion. The algorithm is shown to terminate with a near-optimal assignment in a finite time. Optimality refers to the assignment benefit maximization, which can depend on a target-agent pair value, and the routing cost of the agent to visit the target.

AB - We consider an agent-target assignment problem in an unknown environment modeled as an undirected graph. Agents incur cost or reward while traveling on the edges of this graph. Agents do not know the graph or the locations of the targets on it. However, they can obtain local information about these by local sensing and communicating with other agents within a limited range. To solve this problem, we come up with a new distributed algorithm that integrates Q-Learning and a distributed auction. The Q-Learning part helps estimate the assignment benefits calculated by summing up rewards over the graph edges for each agent-target pair, while the auction part takes care of assigning agents to targets in a distributed fashion. The algorithm is shown to terminate with a near-optimal assignment in a finite time. Optimality refers to the assignment benefit maximization, which can depend on a target-agent pair value, and the routing cost of the agent to visit the target.

UR - http://www.scopus.com/inward/record.url?scp=79953150786&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79953150786&partnerID=8YFLogxK

U2 - 10.1109/CDC.2010.5718180

DO - 10.1109/CDC.2010.5718180

M3 - Conference contribution

AN - SCOPUS:79953150786

SN - 9781424477456

T3 - Proceedings of the IEEE Conference on Decision and Control

BT - 2010 49th IEEE Conference on Decision and Control, CDC 2010

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 49th IEEE Conference on Decision and Control, CDC 2010

Y2 - 15 December 2010 through 17 December 2010

Weapon target assignment strategy for multi-missile cooperative guidance with auction algorithm

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A distributed joint-learning and auction algorithm for target assignment

Profile image of Minghui Zhu

2010, Decision and Control (CDC), …

We consider an agent-target assignment problem in an unknown environment modeled as an undirected graph. Agents do not know this graph or the locations of the targets on it. However, they can obtain local information about these by local sensing and communicating with other agents within a limited range. To solve this problem, we come up with a new distributed algorithm that integrates Q-Learning and a distributed auctions. The Q-Learning part helps estimate the assignment benefits for each agent-target pair, while the auction part takes care of assigning agents to targets in a distributed and almost optimal fashion. The algorithms are shown to terminate with a near-optimal assignment in a finite time.

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Masha Borak

Inside Safe City, Moscow’s AI Surveillance Dystopia

A surveillance camera in Red Square Moscow with the Kremlin in the background

Sergey Vyborov was on his way to the Moscow Metro’s Aeroport station last September when police officers stopped him. The 49-year-old knew that taking the metro could spell trouble. During a protest against Russia’s invasion of Ukraine, police had fingerprinted and photographed him. He’d already been detained four times in 2022. But he was rushing to his daughter’s birthday, so he took a chance.

Vyborov wasn’t arrested that day, but the police informed him that he was under surveillance through Sfera, one of Moscow’s face recognition systems, for participating in unsanctioned rallies. Considered one of the most efficient surveillance systems, Sfera led to the detention of  141 people last year. “Facial recognition, and video cameras in general in a totalitarian state, are an absolute evil,” Vyborov says. 

Vyborov finds himself at the bottom of a slippery slope that privacy advocates have long warned about . Under the guise of smart city technology, authoritarian and democratic governments have rolled out huge networks of security cameras and used artificial intelligence to try to ensure there is no place to hide. Cities have touted the ability of such systems to tackle crime, manage crowds, and better respond to emergencies. Privacy campaigners say such systems could be used as tools of oppression. In Moscow, Vyborov and countless others now face that oppression on a daily basis.

The Russian capital is now the  seventh -most-surveilled city in the world. Across Russia, there are an  estimated 21 million surveillance cameras, and the country ranks among the top in the world in terms of the number of connected surveillance cameras. The system created by Moscow’s government, dubbed Safe City, was touted by city officials as a way to streamline its public safety systems. In recent years, however, its  217,000 surveillance cameras, designed to catch criminals and terrorists, have been turned against protestors, political rivals, and journalists. 

“Facial recognition was supposed to be the ‘cherry on top,’ the reason why all of this was built,” says a former employee of NTechLab, one of the principal companies building Safe City’s face recognition system.

Following Russia’s invasion of Ukraine, Safe City’s data collection practices have become increasingly opaque. The project is now seen as a tool of rising digital repression as Russia wages war against Ukraine and dissenting voices within its own borders. It is an example of the danger smart city technologies pose. And for the engineers and programmers who built such systems, its transformation into a tool of oppression has led to a moment of reckoning. 

Founded in 2015, NTechLab  caught the attention of the global press with the February 2016 launch of FindFace, an app that allowed anyone to identify faces by matching them with images gathered from social network  VKontakte , Russia’s Facebook equivalent. Met with warnings of the  “end to public anonymity,” the app was reportedly downloaded by 500,000 people within two months of its launch. But for NTechLab, it was primarily a proof of concept for its nascent face recognition algorithm.

NTechLab still felt like a startup when one former employee, who asked not to be named for privacy reasons, joined the company. And he was drawn in by the complexity of the work.

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“From [an] engineering point of view, it’s very interesting to work with: It’s very difficult,” he says. 

After the release of FindFace, NTechLab began selling its face recognition tech to small businesses, such as shopping malls that could use it to catch shoplifters or see how many people return to certain stores. But NTechLab was also working with the Moscow Department of IT Technology (DIT), the government department tasked with building Moscow’s digital infrastructure. In 2018, when Russia hosted the FIFA World Cup, NTechLab’s face recognition tech was connected to more than 450 security cameras around Moscow, and its tech  reportedly helped police detain 180 people whom the state deemed “wanted criminals.”

At its inception, Moscow’s face recognition system was fed official watchlists, like the database of wanted people. The system uses these lists to notify the police once a person on the list is detected, but law enforcement can also upload an image and search for where a person has appeared. Over the years, security and law enforcement agencies have compiled a database of the leaders of the political opposition and prominent activists, according to Sarkis Darbinyan, cofounder of digital rights group Roskomsvoboda, which has been campaigning for a suspension of the technology. It remains unclear who is in charge of adding activists and protesters to watchlists.

In March 2019, following the success of the World Cup trial—some of Russia’s “most wanted” people were arrested while trying to attend matches—the Moscow Department of Transportation, which operates the city’s metro, launched its own surveillance system, Sfera. By October 2019, 3,000 of the city’s 160,000 cameras were enabled with face recognition tech, according to interior minister Vladimir Kolokoltsev.

NTechLab was one of many companies building the slew of systems that would later be branded Safe City. International companies, from US tech firms such as Nvidia, Intel, and Broadcom to South Korea’s Samsung and Chinese camera maker Hikvision, worked alongside local firms such as  HeadPoint ,  Netris , and  Rostelecom  that have developed various components of the surveillance systems. According to procurement documents cited by the UK’s BBC, three companies besides NTechLab created face recognition tech for Moscow’s growing surveillance apparatus, including Tevian, and Kipod, and VisionLabs. Moscow's Transportation Department said in social media posts that Sfera was built using VisionLabs technology, although the company downplays its involvement.

NtechLab says it operates in compliance with local laws and does not have access to customer data or camera video streams. Nvidia and Intel say they left Russia in 2022, with Nvidia adding that it does not create software or algorithms for surveillance. Broadcom and Samsung also say they stopped doing business in Russia following the invasion. VisionLabs says it only provides the Moscow Metro with its face recognition payment system. Other companies did not respond to requests for comment. The DIT and the Moscow Department of Transportation did not respond to requests for comment.

At the end of 2018, as Russia cracked down harder on political dissent online and in the streets, the DIT started to change, says a former employee who asked to remain anonymous for safety reasons. The department used to just be the “technical guys” providing assistance to security services, with the Moscow government recruiting highly paid IT specialists to make the most efficient systems possible, according to Andrey Soldatov, an investigative journalist and Russian security services expert. But according to the former employee, the DIT was beginning to reflect the Kremlin’s authoritarian bent.

Then came Covid. 

Safe City launched in 2020, at the height of the Covid-19 pandemic. Russia, like some other countries, seemingly used the pandemic as grounds to expand its surveillance systems to catch people breaking self-isolation rules. By mid-March 2020, Safe City’s face recognition system had  caught 200 people breaking lockdown restrictions. At the same time, Moscow  introduced a regulatory sandbox for the development of AI applications with the participation of large IT companies, exempting authorities from the country’s already lax data protection requirements. “With Covid, [the DIT] essentially became a part of the repressive apparatus,” says Soldatov.

In addition to its network of more than 200,000 cameras, Safe City also incorporates data from  169  information systems, managing data on citizens, public services, transportation, and nearly everything else that makes up Moscow’s infrastructure. This includes anonymized cell phone geolocation data collection, vehicle license plate recognition, data from ride-hailing services, and voice recognition devices. As Safe City was still rolling out in 2020, the Russian government  announced  plans to spend $1.3 billion deploying similar Safe City systems across Russia. From the outside, the potential for the system to be abused seemed obvious. But for those involved in its development, it looked like many other smart city projects. “No one expected that the country would turn into hell in two years,” says one former NTechLab employee, who asked to remain anonymous for safety reasons.

Attempts to break open Moscow’s digital black box have been stonewalled. Alena Popova, whose image was captured during a protest against  politician Leonid Eduardovich Slutsky in April 2018, filed the first lawsuit against Moscow’s DIT for allegedly violating her privacy, seeking a ban on face recognition tech. The case was  thrown out , but Popova has continued to file lawsuits, including one at the European Court of Human Rights—which Russia is  no longer a part of. 

While Moscow operates one of the world’s most pervasive surveillance systems, Russian law does not safeguard individual privacy. With seemingly no hope of recourse, some activists have been forced to leave Russia altogether. Popova is now on the list of foreign agents and is living in an undisclosed overseas location. “I will not apply to any political asylum in any country because I would like to go back to my own country and fight back,” she says.

A key concern is that Moscow’s surveillance system was designed to conceal its data collection from Moscow’s 12 million residents, says Sergey Ross, founder of the Collective Action Center think tank and a former Moscow politician. Although the system is run by the Moscow government, elected members of the Moscow City Duma  say  they are excluded from regulating face recognition systems and have little insight into how it is being used. “It’s a complete black box,” says Ross.

“It was clear that sooner or later the technology would be used to catch activists and dissenters,” says Roskomsvoboda’s Darbinyan. 

Russia made  almost 20,500 political arrests in 2022 , according to data from human rights media organization OVD-Info, which characterizes the number as “unprecedented.” The arrests have sparked fears that Safe City will be expanded to catch draft dodgers—although former NTechLab employees say that doing so would be technically difficult to pull it off because of too many false positives. Still, Moscow police appear to be using face recognition to aid Russia’s war efforts in other ways.

In September 2022, just after Putin announced additional mobilization for the war against Ukraine, Viktor Kapitonov, a 27-year-old activist who’d protested regularly since 2013, was stopped by two police officers after being flagged by face recognition surveillance while he approached the turnstiles in Moscow’s marble-covered Avtozavdodskaya metro station. The officers took him to the military recruitment office, where around 15 people were waiting to enlist in Putin’s newly announced draft. 

“They let me in without waiting in line as if I were some sort of VIP person,” he says. The recruiters wanted to force Kapitonov to enlist, but he ended up escaping the draft. “I explained that I am not fit, I have a disability.”

From 2017 to 2020, NTechLab became one of Russia’s  fastest-growing companies. Other face recognition firms have cashed in as well: The revenue of Russian face recognition developers  grew between 30 and 35 percent in 2022, thanks in part to deals struck in the Middle East, Southeast Asia, India, and South America. Russia’s national AI strategy has supported such firms with grants, tax exemptions, and subsidies, which have benefited both startups and state corporations, including tech and finance giant Sber , telecom provider  Rostelecom , and defense firm Rostec, which previously owned a minority stake in NTechLab. While NTechLab continues to work globally, reporting a revenue increase of 35 percent in 2022, it has also faced a backlash against its work with the Russian state.

In June of last year, a “name-and-shame” list of NTechLab employees was  published [in Russian] with information collected from social media. The project went viral, and some employees reported being harassed online. Artem Zinnatullin, a software engineer now based in the US, says he published the list after NTechLab  sold its new  silhouette recognition technology to the Moscow government in June 2022. To him, it signaled support for Russia’s war in Ukraine. In the post, he called NTechLab “the blacksmith of the Digital Gulag.” Zinnatullin, who says he knew people arrested with the help of face recognition technology, believes publishing the list of NTechLab employees was only fair. “You recognize people on the street, it’s only fair if we use public data to recognize who you are,” he says.

Unlike many face recognition companies that keep a low profile, NTechLab’s splash with FindFace has turned it into a recognized brand. Employees say this high profile has made them into scapegoats. 

As arrests of activists and politicians mounted, the ethics of NTechLab’s technology became a recurring topic at company meetings. NTechLab staff have resisted the use of the company’s face recognition in rallies and refused to sell the technology to the military, according to people familiar with these discussions. Still, the NTechLab leadership concluded that the technology was ultimately positive—even if the occasional dissenting voice was arrested because of it. 

“We all saw these positive examples, we saw how it really catches criminals,” says one former NTechLab employee. “Most people in NTechLab would say they were doing something very good, technologies that can help and save people’s lives. It really did.”

As Russia furthered its march toward authoritarianism in 2021, NTechLab leadership began talking about moving the company abroad, according to people familiar with internal company discussions. But with lucrative government contracts abounding—NTechLab  received a $13 million investment from the Russian Direct Investment Fund, the country’s sovereign wealth fund, in September 2020—its investors resisted the idea. The company was also changing. Its founders, Alexander Kabakov and Artem Kukharenko, stepped down from NTechLab—and both left Russia in December 2021 and February 2022, respectively, declaring   their  anti-war stance on social media. 

Other employees left amid an exodus of IT talent from Russia. The war changed how they viewed their work. “Looking back, we realize that we shouldn’t have done it,” says an NTechLab employee. “But even in 2017 and 2018, it was a completely different country. At least, that’s how it seemed to those who weren’t very immersed in politics.”

Russia’s Safe City projects show no sign of slowing. As more surveillance systems are deployed across the country, Moscow’s DIT is planning to centralize video streams collected across all regions into its own system. And new projects to digitize public services may make it even easier for the government to eventually create large databases where everyone can be found, according to Popova. “It is really scary,” she says. “If they digitalize all the databases and combine them to make this joint database, they can find everybody.” In July, Putin  signed a federal law that funnels personal biometric data collected in the country into a single system—an effort to obtain an “almost unlimited monopoly” on the collection and storage of biometrics, says Roskomsvoboda’s Darbinyan. 

In a further expansion of the Safe City project, Rostec is also  reportedly  developing software that will help authorities predict riots and prevent their escalation by analyzing media reports, data from social networks, video cameras, and other sources. Rostec did not respond to a request for comment on its development of these systems.

Similar systems have been developed in some Chinese cities, and Russia is now playing catch-up. “The Russian government would probably like to move toward China, but they do not yet have the necessary technology,” says Kiril Koroteev, head of international practice at the Russia-based Agora International Human Rights Group.

For now, many activists in Russia are left to do whatever they can to skirt the country’s growing surveillance apparatus, including avoiding the Moscow Metro. Kapitonov hopes that a balaclava will keep him safe, while Vyborov aims to ride the metro early in the morning, when there are fewer police around to detain him. 

“I think that it was inevitable that such a system would be made sooner or later,” says one former NTechLab employee. Face recognition is like a knife, he says: It can be used to cut food, but it can also be used to cut innocent people. He now regrets that NTechLab played a key role in building Moscow’s Safe City project. He has left Russia and doesn’t think he will work on face recognition again. “I do not want to mess with it anymore,” he says.

Update 9:25 am ET, February 6, 2023: Clarified the role of VisionLabs in the Sfera system and that NTechLab's founders have since left the company.

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RealPage algorithm accused of artificially increasing rents, stifling competition

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IMPROVE PAGE

An algorithm called YieldStar is responsible for artificially driving up prices and stifling competition across the US, according to a ProPublica investigation.

The investigation found that approximately 90% of the 31,000+ property managers/landlords who use YieldStar obey its price increase suggestions. Many landlords do not negotiate with their tenants - something discouraged by RealPage - and thereby sacrifice their relationships and trust with them.

ProPublica also alleged that RealPage's software may be facilitating collusion amongst its clients, particularly in locations where many of them use YieldStar to set rent prices.

The article prompted three senators and seventeen members of congress (pdf) to request the US Department of Justice ( D oJ) to investigate RealPage, a move repeated (pdf) in March 2023. Media reports indicate the DoJ opened an investigation late November 2022.

Nine renters also filed (pdf) a lawsuit in San Diego accusing RealPage of forming 'a cartel to artificially inflate the price of and artificially decrease the supply and output of multifamily residential real estate leases from competitive levels.'

In October 2023, the District of Colombia sued (pdf) RealPage and 14 landlords for illegally colluding 'to artificially raise rents by participating in a centralized, anticompetitive scheme, causing District residents to pay millions of dollars above fair market prices.'

Transparency

RealPage acknowledges that YieldStar recommends a price for every available unit every day, determining the rate by drawing on private competitor data on the actual rent tenants paid, as opposed to the publicly advertised rent. 

However, the company will not reveal its inner workings, or open its algorithm to third-party inspection or assessment.

Operator: Greystar; FPI Management; Equity Residential, Lincoln Property Company; Mid-America Apartment Communities Developer: RealPage Country: USA Sector: Real estate sales/management Purpose: Calculate rent prices Technology: Pricing algorithm Issue: Business model; Competition/price fixing Transparency: Governance; Black box

RealPage website

RealPage Wikipedia profile

RealPage AI revenue management  

RealPage statement  

Legal, regulatory

District of Colombia v RealPage (pdf)

Elizabeth Warren DoJ letter (2023) (pdf)

Hausfeld legal complaint (2022) (pdf)

Investigations, assessments, audits

ProPublica (2022). Rent Going Up? One Company’s Algorithm Could Be Why

ProPublica (2022). Why Rent is S o High

ProPublica (2022). Department of Justice Opens Investigation Into Real Estate Tech Company Accused of Collusion with Landlords

ProPublica (2022). Company That Makes Rent-Setting Software for Apartments Accused of Collusion, Lawsuit Says

News, commentary, analysis

https://futurism.com/rental-prices-algorithm-yieldstar

https://www.seattletimes.com/business/rent-going-up-one-companys-algorithm-could-be-why/

https://www.dmagazine.com/frontburner/2022/10/realpage-landlords-sued-by-renters-after-propublica-story/

https://www.zmescience.com/science/news-science/this-clever-algorithm-may-be-whats-driving-rent-prices-so-high/

https://www.axios.com/local/dallas/2022/10/18/texas-company-raising-rents

https://gizmodo.com/realpage-yieldstar-high-rent-housing-class-action-suit-1849683731

https://arstechnica.com/tech-policy/2022/10/company-that-makes-rent-setting-software-for-landlords-sued-for-collusion/amp/

https://www.halifaxexaminer.ca/morning-file/rents-are-too-high-and-governments-are-taking-all-the-wrong-actions-while-ignoring-the-right-one/

https://www.dallasnews.com/business/real-estate/2022/10/18/is-realpages-pricing-software-to-blame-for-soaring-rent-across-the-us/

https://www.theverge.com/2022/11/26/23479034/doj-investigating-rent-setting-software-company-realpage

Faulty automated background checks freeze out renters

Zillow Offers house price automation

Page info Type: Incident Published: October 2022 Last u pdated: February 2024

COMMENTS

  1. A Multi-Target Consensus-Based Auction Algorithm for Distributed Target

    Target assignment is critical in multi-aircraft BVR air combat decision-making. Most previous research on target assignment for multi-aircraft cooperative BVR air combat has focused on centralized algorithms, which can be time-consuming and unreliable.

  2. Weapon Target Assignment Based on Compensation Auction Algorithm

    pp 4622-4631 Home Advances in Guidance, Navigation and Control Conference paper Weapon Target Assignment Based on Compensation Auction Algorithm Xuheng Li, Jianglong Yu, Xiwang Dong, Qingdong Li, Yongzhao Hua & Zhang Ren Conference paper First Online: 31 January 2023 76 Accesses 1 Citations

  3. Improved Auction Algorithm for Weapon Target Assignment with Firepower

    Abstract: This paper proposes an improved auction algorithm to optimize the efficiency of dynamic weapon target assignment (WTA) problem. Firstly, the dynamic battlefield environment of firepower-health model is analyzed and the constraints of weapon assignment problem are established.

  4. Auction algorithm approaches for Dynamic Weapon Target Assignment

    Auction algorithm approaches for Dynamic Weapon Target Assignment problem Abstract: This article takes the multi-weapon formation coordination attack ground target as the background. We have established the mathematical model of the ground maneuvering target with the constraint space factor.

  5. PDF Weapon Target Assignment Based on Compensation Auction Algorithm

    This algorithm simulates the trading rules in the market, regards the task assignment like an auction, and realizes WTA as an auction mechanism. [10] considered dynamic constraints, modelled in a three-dimensional scene, and factored in cooperative combat during a missile attack.

  6. Sensor-target and weapon-target pairings based on auction algorithm

    The problem can be considered as an assignment optimization problem in mathematics. This problem is difficult because in the real world it involves many different factors and criteria to consider. We show that for practical sensor-target and weapon-target pairings a well-known auction algorithm should be considered the preferred choice.

  7. A Multi-Target Consensus-Based Auction Algorithm for Distributed Target

    This paper proposes an efficient distributed target assignment algorithm called the multi-target consensus-based auction algorithm (MTCBAA). First, by analyzing the main geometric...

  8. Research on Distributed Target Assignment Based on Dynamic ...

    In this paper, a distributed target allocation method based on dynamic allocation auction algorithm is used to solve this kind of problem. This paper establishes a mathematical model based on air defense combat, and uses this method to solve the problem.

  9. A distributed joint-learning and auction algorithm for target assignment

    T1 - A distributed joint-learning and auction algorithm for target assignment. AU - Sadikhov, Teymur. AU - Zhu, Minghui. AU - Martínez, Sonia. PY - 2010. Y1 - 2010. N2 - We consider an agent-target assignment problem in an unknown environment modeled as an undirected graph. Agents incur cost or reward while traveling on the edges of this graph.

  10. PDF A Multi-Target Consensus-Based Auction Algorithm for Distributed Target

    target assignment algorithm simultaneously to obtain their respective assignment solutions. This type of algorithm does not require a central node and, therefore, improves system ... Another way to realize distributed target assignment is to use an auction mechanism. A typical algorithm of this type is the contract network-based auction ...

  11. Weapon Target Assignment Based on Compensation Auction Algorithm

    Weapon-target assignment (WTA) which is crucial in cooperative air combat explores assigning weapons to targets with the objective of minimizing the threats from those targets. Based on threat...

  12. Weapon target assignment strategy for multi-missile cooperative

    Weapon target assignment strategy for multi-missile cooperative guidance with auction algorithm Abstract: This paper presents a weapon target assignment method to reduce maneuverability overload through redistribution for multi-missile cooperative attack.

  13. Weapon target assignment strategy for multi-missile cooperative

    Request PDF | On Jul 26, 2021, Qingze Li and others published Weapon target assignment strategy for multi-missile cooperative guidance with auction algorithm | Find, read and cite all the research ...

  14. Sensor-target and weapon-target pairings based on auction algorithm

    The problem can be considered as an assignment optimization problem in mathematics. This problem is difficult because in the real world it involves many different factors and criteria to consider. We show that for practical sensor-target and weapon-target pairings a well-known auction algorithm should be considered the preferred choice.

  15. A distributed joint-learning and auction algorithm for target assignment

    The Q-Learning part helps estimate the retical significance [1], [3], [8]. One of the most widely- assignment benefits for each agent-target pair, while the auction used assignment algorithms is the auction algorithm first part takes care of assigning agents to targets in a distributed and almost optimal fashion.

  16. Inside Safe City, Moscow's AI Surveillance Dystopia

    The system created by Moscow's government, dubbed Safe City, was touted by city officials as a way to streamline its public safety systems. In recent years, however, its 217,000 surveillance ...

  17. Improved Auction Algorithm for Weapon Target Assignment ...

    Request PDF | On Jul 24, 2023, Shuobo Wang and others published Improved Auction Algorithm for Weapon Target Assignment with Firepower-Health Model | Find, read and cite all the research you need ...

  18. Online Weapon-target Assignment based on Distributed Auction Mechanism

    To solve the problem of online weapon-target assignment (OWTA) in the integration of large-scale search and attack in unknown environment, an OWTA algorithm based on distributed auction mechanism is presented. Aiming at the problem that the traditional combinatorial optimization algorithm needs to set up the global battlefield situation in ...

  19. The artful task of selling the Moscow police station

    Moscow, Idaho. Photo by Steven Pavlov, CC BY-SA 3.0 via Wikimedia Commons The Moscow City Council gave the go-ahead on Aug. 2 to draw up the purchase agreement to sell its old police station to the University of Idaho. The sale caps two years of musical chairs involving the city, the university and multiple Moscow

  20. Auction algorithm approaches for Dynamic Weapon Target Assignment

    Auction algorithm approaches for Dynamic Weapon Target Assignment problem | Semantic Scholar DOI: 10.1109/ICCSNT.2015.7490778 Corpus ID: 15645288 Auction algorithm approaches for Dynamic Weapon Target Assignment problem Jun Chen, Jianwen Yang, Ye Guanfeng Published in International Conference on… 1 December 2015 Computer Science, Engineering TLDR

  21. AIAAIC

    Lawsuit claims Amazon Buy Box algorithm overcharges shoppers. Amazon sells AI-generated books about King Charles' cancer. Toilet sensors 'actively listen' to UK school pupils. New York lawyer cites fake AI-generated court case. American Bitcoin Academy charged with 'AI' powered fraud. Google researcher believes LaMDA is 'sentient'

  22. AIAAIC

    An algorithm called YieldStar is responsible for artificially driving up prices and stifling competition across the US, according to a ProPublica investigation.. The investigation found that approximately 90% of the 31,000+ property managers/landlords who use YieldStar obey its price increase suggestions. Many landlords do not negotiate with their tenants - something discouraged by RealPage ...