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In the book Artificial Intelligence: A Modern Approach (section 5.7, p. 185), Russell and Norvig write In 1965, the Russian mathematician Alexander Kronrod called chess "the Drosophila of artificial intelligence." John McCarthy disagrees: whereas geneticists use fruit flies to make discoveries that apply to biology more broadly, AI has used chess ...


9

Not all games (or even board games) are computationally algorithmic. Even the least skilled player is likely to trounce the hottest pattern-matching algorithm in a game of Pictionary (for example). If you want to say that the movement of pieces upon successful completion of a task is only ancelary to the object of the game, than your answer will be largely ...


7

This relates to the concept of "solved games". In general, two player turn-based games with perfect information - of which chess is an example - can result in all three possible outcomes: a forced win for white, a forced win for black, or a forced draw. The short, although unsatisfactory answer is that chess is not solved, and it is not clear whether it can ...


7

The allegation was based on the fact that Deep Blue made a choice that did not yield the immediate (or short term) benefit that was synonymous with systems back then (1997). Computational capability was significantly less powerful then, and Kasparov claimed that only a grand master would have made the decision that the system did - so the deep blue team ...


7

For many years, the focus has been on games with perfect information. That is, in Chess and Go both of us are looking at the same board. In something like Poker, you have information that I don't have and I have information that you don't have, and so for either of us to make sense of each other's actions we need to model what hidden information the other ...


6

For this, we will need game theory. In game theory, an optimal strategy is one that cannot be exploited by the opponent even if they know your strategy. Let's say you want a strategy where your move selection is not based on what happened before (so you are not trying to model your opponent, or trick them into believing you will always play scissors and then ...


5

These are big areas, so here is a brief description of the differences: Game theory is concerned with studying solutions for 'games', which are basically a set of decisions leading to certain outcomes. In game theory you look at strategies to achieve the best outcome for a given participant. One classic example (which isn't really a game in the traditional ...


4

This may be an evolving answer, because the question is, in some sense, a (useful) rabbit hole. I apologize if I don't go deeply into meta-games per se, as it's a little outside of my scope, which is non-chance games of perfect information, but I think it's worthwhile to think about the underlying problem of indeterminacy in relation to games in general. ...


4

This question is re-inventing the analysis for iterated prisoner's dilemma and the co-evolution that can lead to agents playing super-rationally in the one-shot version, which has been studied really extensively. Dan Ashlock's research career looked at this in great detail from an evolutionary perspective, but it's also been widely studied in other areas ...


3

If the game is not sequential, there would be no game tree and no need for pruning. Alpha-beta is a technique applied to look-ahead search. Alpha-beta has demonstrated utility in algorithms that play combinatorial games. (Even in iterated dilemmas, it doesn't really branch because it's simultaneous, more of a vine than a tree. Decisionmaking would be ...


3

There is indeed a close parallel here, but the concepts are distinct. Every perfect information game is fully observable, but not every fully observable game is a game of perfect information. A game of imperfect information is one in which you lack knowledge of any of the following: The state of the game (e.g. current market prices). The rewards you will ...


3

Catan is actually a much more complicated game than the simple rules would suggest, and an exact solution is probably beyond the scope of current AI techniques. Monte Carlo Tree Search or Expectiminimax techniques seem like they could help, but are intended for games of perfect information. Catan is not a game of perfect information (the development cards ...


3

The game of TIC-TAC-TOE can be modelled as a non-deterministic Markov decision process (MDP) if, and only if: The opponent is considered part of the environment. This is a reasonable approach when the goal is to solve playing against a specific opponent. The opponent is using a stochastic policy. Stochastic policies are a generalisation that include ...


2

Artificially intelligent computer programs should be able to be at the same level or beat humans at every game that we play. This is because games follow rules that are scriptable, and artificial intelligence is designed to focus on one specific game and learn from its failures. The difference between humans and artificial intelligence is that artificial ...


2

This second answer attempts to address perfect play in relation to incomplete information specifically. An element in the difficulty in answering this question may be that the concept of perfect play is widely applied to solved games in the domain of Combinatorial Game Theory as opposed to strictly economic Game Theory. In relation to games with incomplete ...


2

I think you're going to have to be reconciled to the subjective nature of reality. Objectivity is only possible in very special cases such as a Q.E.D. in mathematics, or a solved gamed. Rationality is bounded, and any intractable problem results in a state of subjectivity/indeterminacy. Additionally, pure values do not carry moral implications, despite ...


2

Minimax deals with two kinds of values: Estimated values determined by a heuristic function. Actual values determined by a terminal state. Commonly, we use the following denotational semantics for values: A range of values centered around 0 denote estimated values (e.g. -999 to 999). A value less than the smallest heuristic value denotes a loss for max (e....


2

MOEAs sounds very cool, but I feel that you can't really talk about conflict in AI without discussing generative adversarial networks (GANs), which have been shown to have amazing performance by training a model to say detect in-between pictures of cats and dogs and an adversarial network being trained to create pictures to attempt to trick the training ...


2

There are multi-objective optimization problems, where the objective functions may be in conflict with each other, which can potentially have multiple Pareto-optimal solutions. The paper Multi-objective optimization using genetic algorithms: A tutorial (2006) gives a good overview of the multi-objective optimization problem with genetic algorithms, which can ...


2

Historically, the non-ML approach would be an expert system. This is typically a rules-based decision system, falling under the umbrella of symbolic AI. These systems can have strong utility in limited contexts, but are generally "brittle" in that parameters not previously defined or accounted will produce no-compute or weak utility. Because the rules of ...


2

A few of us have spent quite a bit of time thinking about this. I summarised our work in a Medium article here: https://towardsdatascience.com/deep-learning-vs-puzzle-games-e996feb76162 Would love to hear what you think. Spoiler: so far, good old SAT seems to beat fancy AI algorithms!


2

It's currently just too complex The different sources of information are too varied, in economics this is often referred to as a local knowledge problem, which hampers many large scale plans. Humans can react to slight differences like respecting local traditions, landscapes, history but an artificial intelligence would (currently at least) struggle not to ...


2

Philip's answer is good, but I'll add to it. In a GA, a population of individuals (typically represented by bit strings) is evaluated for its fitness on a particular task. Each individual is evaluated separately by a fitness function than can determine its quality. In the Traveling Salesman Problem, the bit string might represent a sequence of numbers, for ...


2

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

A genetic algorithm is typically a single population designed to optimise to a specific task, say minimising the distance on the travelling salesman problem. Evolutionary game theory algorithms typically model changes between populations that are in competition, generally by using genetic algorithms as above but framed within a broader competitive ...


1

Not exactly, at least traditionally: in Game Theory, "imperfect information" is most often defined as agents having only partial information about the history of agents' actions, as you correctly noted. But also note that this doesn't refer to the general world facts or state. But "partial observability" is typically used in terms of systems, e.g. in Markov ...


1

Leaving aside the time aspect, you could do a cluster analysis on the event coordinates. If you use an algorithm that gives you a medoid (ie centre) of the clusters, you can then look at other points, and work out how close they are to the centres of the event clusters. It might be possible from this to predict which event could happen at those coordinates (...


1

After 4 days of research, this is my breakdown of the question: Human uses the term 'Evil' broadly to describe anything that cause sadness or even broadly anything negatively touch the happiness. So in this regard, any machine quite often called evil if its buggy, malfunctioning or even misused by the user! In order to represent evil in logic, I need to ...


1

Short version below. When implementing a minimax algorithm the purpose is usually to find the best possible position of a game board for the player you call max after some amount of moves. In some games like tic-tac-toe, the game tree (a graph of all legal moves) is small enough that the minimax search can be applied exhaustively to look at the whole game ...


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