9

There has been quite a few approaches to achieve such kind of distributed coordination. I present here one of them, for its generality and simplicity (that makes it easy to remember too). But first, the general idea behind these approaches is pretty interesting, around a mechanism called stigmergy. Stigmergy is a behaviour coordination mechanism mediated by ...


7

There are no distinguishable hardware examples for each IA class. The same mobile robot architecture with proper sensors can be implemented to behave as any IA class. The way you can determine the class of an intelligent agent is from the way it processes the percept. Based on chapter 2 of Artificial Intelligent: A Modern Approach I will try to give a ...


6

Swarm intelligence is the term for systems where relatively simple agents work together to solve a complicated problem in a decentralized fashion. In general, distributed computing methods are very important for dealing with problems at scale, and many of them embrace decentralization in a deep way. (Given the reality of hardware failure and the massive ...


5

Instead of having the AI learn what action to take, you can alternatively train it to judge how "good" a position is. In order to determine what move to make, you don't ask the AI "This is the current state, what move should I make", you iterate through all possible moves, and feed the the resulting state into the AI asking "How good do you think this new ...


4

Filling values is totally fine. In the case of image recognition the filling will be the background of the image (examples). For example in Belot you have total of 32 cards, which can be 32 boolean features. You can set the ones the player has to 1, while the rest are 0. Note that the in most games you'll need more features than the cards in your hand. I.e ...


3

This is a highly relevant question as market trends have become more emphasized over the fundamentals of individual companies, and algorithmic trading has proven to be quite effective, particularly in areas such as high-frequency micro-trading. This 2013 Forbes article estimated nearly 80% of stock trading volume in the U.S. is conducted by automated ...


3

Does it have to do with the reward function? This seems likely to me. You have chosen a reward that is unusual in that it cross-links episodes. It is not really a reinforcement problem to optimise behaviour with respect to results of previous episode behaviour in this way. This might be an option for an evolutionary fitness context, if you have competing ...


3

Considering your use case, I would not use Deep Learning methods... what is the point? Instead of just winning, good AI is fun to play with. In practice when fine tuning game mechanics, you will want to analyze the game for churning events. Then it would be nice, if you could show the AI that "Hey, this is messed up, could you come up with a nice way of ...


2

I think the best example of AI being deployed on the blockchain is SingularityNET. They just had a successful token sell where they sold out of their AGI token which will be able to be used to essentially "pay" for AI tasks to be done for you. Various AI will be put on the network and able to interact and communicate with each other to get various tasks done....


2

Not particularly sure what you are asking, so the question that I will be answering is this: Can Q learning be used to estimate a value that depends on another value in the Q Learning Matrix even though there is a certain amount of unpredictability involved? The answer is yes! I will use the example of a robotic arm trying to reach a point in space ...


2

A very efficient approach to what you are trying to do is velocity obstacles. Assuming two agents use constant velocity motion vectors, a velocity obstacle models a geometric region in which if the endpoint of the velocity vector for agent 1 falls, it will collide with agent 2 (and vice versa). Hence you can predict what velocity vectors will lead to ...


2

A couple of thoughts: Humans can't reliably predict trends in the stock market, so expecting AI's to do so is probably unreasonable. The above would be more true if it were proven that the movement of stock prices is really a random walk, but my understanding is that the current thinking is that stock movements aren't completely random... but just really ...


2

The cleanest solution from a theoretical point of view is to switch over to a hierarchical framework, some framework that supports temporal abstraction. My favourite one is the options framework as formalised by Sutton, Precup and Singh. The basic idea is that the things that you consider "actions" for your agents become "options", which are "large actions" ...


2

I think there is an intersection. There are problems that are in reinforcement learning and in learning in multi-agent systems. There are problems in reinforcement learning, but not exactly in multi-agent systems. And there is learning in multi-agent systems that is not through reinforcement learning. For sort you can say: multi-agent reinforcement learning. ...


1

it can be either. If you consider the lack of reward as "penalty" then getting 0 reward is bad. if you use a value estimator through a neural network, the range of rewards will dictate the squashing function you use for the output layer


1

In MORL the reward component is a vector rather than a scalar, with an element for each objective. So if we are using a multiobjective version of an algorithm like Q-learning, the Q-values stored for each state-action pair will also be vectors. Q-learning requires the agent to be able to identify the greedy action in any state (the action expected to lead ...


1

An agent is a concept, which can have slightly different meanings, abilities or instantiations depending on the context. However, given the purpose of this website, I will use and refer to the definition of agent commonly used in artificial intelligence. An agent is anything that can be viewed as perceiving its environment through sensors and acting upon ...


1

This evening I got inspired by this paper: http://www.dphrygian.com/bin/David-Pittman-GOAP-Masters-Thesis.doc (GOAP paired with the Command and Control Pattern) What do you think about this solution? Each goal has a relevance (that depends on the agent needs) When agent1 working on the "AskAgent2ToTalk" Action, it only sends a goal recommendation to ...


1

In a sense it seems like I am thinking some kind of "Deep Agent-Based Modeling", where it is okay to have network effects on the lowest layer (which would only evaluate the matching of the generated artifacts and their own preferences); the co-operation problem should be solved by a "Coach" layer, which would attempt to prevent situations, where some agents ...


1

There is quite some research done by Hans-Georg Zimmermann, who has programmed Neural Networks for Siemens since some 20 years in order to predict Stock markets. He wrote some books on it, too, though I don't know if they are any good in English. This article gets to the point a bit faster than the video, I hope it helps. edit: I think this interview gives ...


1

In my answer, I have often switched between AGI and ASI for reference. This is fine as an AGI will reach ASI as it is optimizing itself and learning. I think it is not only important by necessary that AGI and ASI are of collaborative nature. Nick Bostrom, in his book Superintelligence: Paths, Dangers, Strategies in Chapter 10 described three ways in which an ...


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