Hostile tactical ai

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Hostile tactical ai

Terms of Service - Privacy Policy. Game Development Tools and Software. Register Login Cart. Game Development Community. What's New Blogs Resources Forums. I have finally accomplished something that was much needed for the Tactical AI Kit--reacting to threats.

So far, it's pretty basic, but I'm quite glad that I implemented in two hours what many games today don't offer with AI. After a while, I grew tired of having my AI Players getting stupidly killed because they weren't facing the right way and couldn't figure out that he was being shot at. My original idea was to make the AI Players cheat; when they come under fire, they know where the threat came from and will immediately react. Nothing bothers me more that being unable to have fun sniping in a game because you instantly come under fire from enemies who shouldn't know you exist.

I was playing co-op Chaos Theory at a friend's house not too long ago, and was sniping an enemy from across a system of docks who was blazing away at my friend. I hit him in the leg from meters or so, with a silenced weapon, and I suddenly came under fire. There is no way he heard that shot across the docks while his assault rifle was going off. That made me mad, not just because I was promptly killed, but because a game that relatively new needed to resort to cheating AI.

Tactical AI Kit: Assault Gameplay

So, I looked for a solution that would allow AI Players to move intelligently while also looking like they are panicking when they come under fire while not engaging an enemy.

At that same friend's house, I was playing the first Ghost Recon game and noticed something interesting. At the bottom of the screen, there is a little compass-thingy that looks like this: Whenever the player comes under fire and is close enough to hear the shot, the section that points closest to the location of the gunshot turns red.

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This gave me an idea. Every 25 milliseconds, the AI Player performs a quick radius search and sees if there are any enemy players firing their weapon okay, okay, some cheating is employed here to get the firer's team, but without it, they would mistake friendly players firing as possible enemies, which looks pretty bad.

This is not as performance-costly as it sounds. Now we check the distance of the shot and the datablock of the weapon that was fired. In the weapon datablock, a field needs to be added that specifies how close an AI Player has to be to hear it firing rifles could have a sound radius of aroundlower distances around 40 or so could be used for silenced weapons, etc. If a gunshot is heard, the location of the shot is altered from 0 to 90 degrees based on the distance of the shot, so that there is some guess error.Hostile Tactical AI is an Artificial Intelligence system to order enemy units around in skirmish miniature wargames, so you may play solo or co-op against the enemy.

The rules work through a deck of 72 cards that tell enemy units, where to go and what to do. There are two sets of instructions on every card. One set gives orders to patrolling units unaware of the player. The other set of orders is for aware units attacking the player's units. Patrol instructions include: Sweeping the area for the player, Patrolling objectives, Checking suspicious behavior, etc Attack instructions include: Rushing the player, Defensive posturing, Flanking, etc Each card includes: Compass directions, Dice rolls, Player to attack, and Reactions.

There are also playing card symbols on each card to be used for variable activations or objective orders in the case of multiple objectives. Included with the deck and rulebook are sixteen shards matching the playing card symbols.

Tactical Shooter AI - Asset Store Pack

These can be used for random setup of enemy units. The rules were specifically designed for miniature games with ranged weapons. For each enemy activation, the player draws a card for that enemy unit. The card gives the enemy order or orders. Sometimes requiring multiple cards to be drawn to show what direction and how far the enemy moves, and also which player to attack.

Hostile Tactical AI is quick to learn and gives you a good fight. The enemy might just surprise you! These PDF files are digitally watermarked to signify that you are the owner. A small message is added to the bottom of each page of the PDF containing your name and the order number of your purchase.

Warning : If any files bearing your information are found being distributed illegally, then your account will be suspended and legal action may be taken against you. Log In. New Account or Log In. Hide my password. Get the newsletter. Subscribe to get the free product of the week! One-click unsubscribe later if you don't enjoy the newsletter. Log In with Facebook. Log In I am new here. Remember me. Error: No match for email address or password.The accelerating complexity of wireless design has resulted in difficult trade-offs and large development expenses.

Some of the complexities of RF system design are innate to radio, such as hardware impairments and channel effects. Another source of complexity is simply the breadth of the degrees of freedom used by radios. Communications and RF parameters — such as antennas, channels, bands, beams, codes, and bandwidths — represent degrees of freedom that must be controlled by the radio in either static or dynamic modalities.

Recent advances in semiconductor technology have made wider bandwidths of spectrum accessible with fewer parts and less power, but these require more computation if real-time processing is important.

Furthermore, as wireless protocols grow more complex, spectrum environments become more contested and electronic warfare EW grows in sophistication, the baseband processing required by military radios becomes more complex and specialized. Taken together, the impairments, degrees of freedom, real-time requirements, and hostile environment represent an optimization problem that quickly becomes intractable.

Indeed, fully optimizing RF systems with this level of complexity has never been practical. In the last few years, there have been significant advances in AI, especially in a class of machine-learning techniques known as deep learning DL.

Where human designers have toiled at great effort to hand-engineer solutions to difficult problems, DL provides a methodology that enables solutions directly trained on large sets of complex problem-specific data. A designer would write algorithms that first attempted to locate eyes in an image, and once those were found, the nose, the mouth, the jawline and ears, and then the outline of the entire face.

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The algorithm was hand-designed, based on years of research into what techniques were effective. The DL approach is dramatically different.

Continuing the facial-recognition example, an engineer simply feeds a dataset of images containing faces to a DL model and tells it where the faces are in the training data. Through the training process, the machine learns to recognize faces in images without a human specifying the features or algorithms by which to make decisions.

Furthermore, where a human might struggle algorithmically with differences and complexity in the data e. DL also enables end-to-end learning, which refers to training a model that jointly optimizes both ends of an information flow, treating everything in between as a unified system.

For example, a model can jointly learn an encoder and decoder for a radio transmitter and receiver that optimizes over the end-to-end system e. Indeed, this is often the usage pattern when integrating DL into existing systems where there is less flexibility in the processing flow.

A key advantage of end-to-end learning is that instead of attempting to optimize a system in piecemeal fashion by individually tuning each component and then stitching them together, DL is able to treat the entire system as an end-to-end function and learn optimal solutions over the combined system holistically.The acclaimed series from Don Lomax, nominated for a Harvey Award, is now presented as a series of graphic novels that collects the entire series.

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hostile tactical ai

Publisher Resources. Time Period. STL 3D Model. Vector Graphics Laser Cutter. A Fistful of Games. Ad Astra Games. Admiralty Trilogy Group. Battlespace Games. Blue Panther LLC. Brent Spivey Creations. Dave Graffam Models.

hostile tactical ai

Flying Pig Games. MicroMark Army Lists. Nordic Weasel Games. Past Into Print. Rory Crabb. Skirmisher Publishing. Sundered Vault. Tiny Battle Publishing. Pay What You Want.Hostile Tactical AI is an Artificial Intelligence system to order enemy units around in skirmish miniature wargames, so you may play solo or co-op against the enemy.

The rules work through a deck of 72 cards that tell enemy units, where to go and what to do. There are two sets of instructions on every card. One set gives orders to patrolling units unaware of the player. The other set of orders is for aware units attacking the player's units. Patrol instructions include: Sweeping the area for the player, Patrolling objectives, Checking suspicious behavior, etc Attack instructions include: Rushing the player, Defensive posturing, Flanking, etc Each card includes: Compass directions, Dice rolls, Player to attack, and Reactions.

There are also playing card symbols on each card to be used for variable activations or objective orders in the case of multiple objectives. Included with the deck and rulebook are sixteen shards matching the playing card symbols. These can be used for random setup of enemy units. The rules were specifically designed for miniature games with ranged weapons.

For each enemy activation, the player draws a card for that enemy unit. The card gives the enemy order or orders.

Sometimes requiring multiple cards to be drawn to show what direction and how far the enemy moves, and also which player to attack. Hostile Tactical AI is quick to learn and gives you a good fight. The enemy might just surprise you! Why buy this?

Tactical AI Kit: Reacting to Threats

SixJAM contact us. Notes This game contains a premium upgrade called UV Coating that makes the printed components more durable. This game contains laser cut components.

Laser cut items will have a slight amount of soot around the edges, which can easily be wiped off; and will have a campfire smell for about a week after you open them. Ratings and Reviews Be the first to review this!

Please Sign In to leave a review. All rights reserved.Our core technology, Tactical AI, enables robust autonomous decision making capabilities in these six targeted domains with the highest potential for impact. Click the circles on the right to learn more about each technology. For many time-critical RF systems such as software defined radios and multi-function RF Systems that require processing vast amounts of information in real time within stringent timing constraints, it is absolutely critical to maximize the usage of multiple heterogeneous computing resources e.

Tactical AI automatically learns to enact a scheduling resource allocation action as a function of real-time monitoring of operational parameters such as processor utilization, available data transfer, power consumption, and computing latency by training its DRL model with feedback from other expert-based models or from scratch. Simultaneously, Tactical AI enacts its autonomous rule-based learning engine i. A2RL to learn the training environment as a whole so that it can autonomously detect deviations from the norm learned from training during the actual operation.

In order to address the explosively increasing demand for RF spectrum access, a new wireless paradigm must be developed for autonomous, collaborative, and local technologies to share the spectrum without strict frequency allocations. Cognitive radio networks must reason how to avoid interference and exploit opportunities to achieve efficient use of the available spectrum.

Intelligent CRN technologies must take advantage of recent advances in machine learning and the expanding capacities of software defined radios, to produce breakthroughs in collaborative AI and catalyze the advent of a new era of spectrum abundance. Tactical AI agents effectively capture a wide range of spectrum holes through shifts in time and frequency via an extensive amount of data from both simulation and real-world tests.

These agents will detect unforeseen trends and behaviors of the spectrum usage dynamics that were not learned during their training phase, and adapt to them on-the fly. Then Tactical AI will automatically adapt the available radio control strategies such as control channel rendezvous, frequency selection for data communication, modulation scheme, etc. The potential benefit of collaborative MUM-T systems can be fully realized if mission-critical data is reliably and efficiently shared among all engaged assets throughout a mission.

Building a practical mission-cognitive capability for future MUM-T systems is critical for its viability and effectiveness. When surprises occur, the Tactical AI agents deployed across the MUM-T assets would analyze the surprises and select alternative actions within the given bounding box and continue the planned mission.

Building and maintaining an accurate RF landscape in the presence of many unknown signals of interest is the foundation for developing effective EW strategies for both defense and offense.

Tactical AI builds and maintains comprehensive RF situational awareness knowledge in frequency and time across all wireless communication and networking protocols without computationally expensive DSP algorithms.

Tactical AI learns the characteristics of important signals directly from the digitized radio signal e. Due to the increasingly sophisticated and aggressive level of cyber threats, network defenders have to have state-of-the-art capabilities to protect beyond a prescribed set of cyberattacks from both insiders and outsiders.

Unfortunately, traditional in-person cybersecurity training lacks realism and scalability to keep pace with the adversary as they increasingly evolve their attacks with the help of machine learning. Training a large number of network defenders with human trainers with the latest knowledge of cyber attacks is extremely costly and time consuming. Tactical AI is initially trained to observe the current situation and perform the most appropriate cyber-attack, including disengaging an ongoing attack to avoid detection.

Resource Optimization for Time-Critical Computing For many time-critical RF systems such as software defined radios and multi-function RF Systems that require processing vast amounts of information in real time within stringent timing constraints, it is absolutely critical to maximize the usage of multiple heterogeneous computing resources e. Tactical AI is the first neural resource optimizer to take into account heterogeneous computing resources, extremely tight timing, and energy consumption requirements.

The resource optimization by Tactical AI significantly outperforms the latest state-of-the-art heuristic optimizers while protecting itself from untrained situations. Contact us to learn more about our innovative resource optimization with Tactical AI. Cognitive Radios and Networks CRNs In order to address the explosively increasing demand for RF spectrum access, a new wireless paradigm must be developed for autonomous, collaborative, and local technologies to share the spectrum without strict frequency allocations.

Our Tactical AI-enabled CRN systems achieve maximum spectrum sharing efficiency by eliminating common overheads associated with channel rendezvous, collision avoidance handshakes, and quasi-static frequency division multiple access FDMA. Tactical AI employs an error correction scheme that maximizes the broadcast nature of wireless medium while minimizing redundancy overhead such as source coding, retransmission, or multi-path routing; Tactical AI controls the amount of redundancy needed to be injected into the network to compensate errors based on the continuous environment sensing, learning and re-learning.

Tactical AI-enabled MUM-T systems enjoy significantly more robust and effective collaborative autonomous systems between humans and autonomous nodes. When surprises occur, the Tactical AI agents would analyze the surprises and select alternative actions within the given bounding box and continue the planned mission.Artificial intelligence AI is intellect displayed by a machine contrary to the natural intelligence NI displayed by a human being. The applications of AI are both numerous and varied.

Businesses and organizations worldwide are working faster and smarter due to AI and one such domain is the military. Subscribe and get this detailed guide absolutely FREE.

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ByChina aims at achieving great strides in various AI technologies like big data and autonomous intelligent systems.

All of this would have an estimated value of more than billion RMB. As far as the US Government is concerned, it has been spending billions of dollars to capitalize on AI as the next best thing in warfare.

hostile tactical ai

Drones, motherships, protective exoskeletons for troops, unmanned vessels that can carry out missions and combat apps that can execute commands are some of the military-based AI applications in US and China. Adopting AI tools in defense enhances the processing and utilization of data which in turn improves the speed of decision-making on the battlefield.

Below are some of the top 5 applications of AI in the military:. Computational military reasoning solves military problems that humans face and focuses on making the right battlefield decisions. It analyzes the area of conflict and acts on the data it received from the set of orders known as Course of Action or COA.

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In controlled settings like military labs, this application has been able to exploit weaknesses of the enemy successfully. Unmanned vehicles that are used by armed forces, as well as the most sophisticated weapons and robotics, are some of the direct uses of AI.

These weapon systems have the requisite intelligence to observe, pursue and destroy enemy targets distinctly. Deep learning algorithms can be used to effectively process sensor data and raw intelligence that was collected from satellite imagery. All this information can in turn aid in making accurate decisions during an operation.

Cyber attacks can be detected and curbed through pattern matching, statistical analysis, machine learning and big data analysis. Offensive cyber operations unleash a large scale of destruction in a matter of minutes. Considering the speed at which AI works, it serves as a counter-intuitive force against malicious cyber threats. EW employs the electromagnetic spectrum that includes radio waves, infrared signals or radar to sense, protect, and communicate data. It can be also used to block enemies from using signals.

ARC enables airborne EW systems to automatically generate effective spontaneous countermeasures against unknown radars in real time.

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This is done intelligently in the presence of other signals which can be either hostile or neutral. On the other hand, the BLADE program develops machine learning algorithms to rapidly detect new radio threats and dynamically generate new countermeasures that are similar to ARC even in the most tactical environments.

However, the number of skilled professionals in this field is quite limited considering the vast range of opportunities it offers. If you are enthusiastic about learning artificial intelligence, you should definitely check out these courses that cover extensively about deep and machine learning. Human is evolving yet in bringing technological revolutions.

Artificial intelligence or AI is a technology that deals with computer science that directs in the making of intelligent machines that work and respond like humans. New technologies are emerging regularly using AI. This is wonderful article with upcoming AI projects.

I am obliged for this information. Artificial Intelligence is the Next big thing and it will be going to play an important role in the future like many industries already applying it for their market research.

AI and military RF systems

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