Equilibrium : The point in a game where both players have made their decisions and an outcome is reached. Agent : Agent is equivalent to a player.
Reward : A payoff of a game can also be termed as a reward. State : All the information necessary to describe the situation an agent is in. Action : Equivalent of a move in a game. Policy : Similar to a strategy. It defines the action an agent will make when in particular states Environment : Everything the agent interacts with during learning. Different Types of Games in Game Theory In the game theory, different types of games help in the analysis of different types of problems.
Cooperative and Non-Cooperative Games Cooperative games are the ones in which the players are convinced to adopt a particular strategy through negotiations and agreements between them. Normal Form and Extensive Form Games Normal form games refer to the description of the game in the form of a matrix. Simultaneous Move Games and Sequential Move Games Simultaneous games are the ones in which the move of two players the strategy adopted by two players is simultaneous.
Constant Sum, Zero Sum, and Non-Zero Sum Games Constant sum games are the ones in which the sum of outcome of all the players remains constant even if the outcomes are different. Symmetric and Asymmetric Games Symmetric games are the ones where the strategies adopted by all the players are the same.
Game Theory in Artificial Intelligence Development of the majority of the popular games which we play in this digital world is with the help of AI and game theory.
Nash Equilibrium Nash equilibrium, named after Nobel winning economist, John Nash, is a solution to a game involving two or more players who want the best outcome for themselves and must take the actions of others into account. Conclusion So in this article, the fundamentals of Game Theory and essential topics are covered in brief.
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In the multi-agent case, however, the proofs lose their validity. Just to illustrate some of the mind-puzzling difficulties that arise: an agent executes a learning algorithm to learn how to react optimally to its environment. In our case, the environment includes the other agents, which also execute the learning algorithm.
Thus, the algorithm has to consider the effect of its action before it acts. The concerns start where game theory started: in economics. Rationality: generally in game theory, and in order to derive Nash equilibria, perfect rationality is assumed.
This roughly means that agents always act for their own sake. Complete information: each agent knows everything about the game, including the rules, what the other players know, and what their strategies are. Common knowledge: there is common knowledge of a fact p in a group of agents when: all the agents know p , they all know that all agents know p , they all know that they all know that all agents know p , and so on ad infinitum.
There are interesting puzzles, like the blue-eyed islanders , that describe the effect common knowledge has on a problem. If you find that Arrow is a bit harsh with classical game theory, how rational would you say your last purchases have been? Or, how much consciousness and effort did you put into your meal today?
But Arrow is not so much worried about the assumption of rationality. He is worried about the implications of it. For an agent to be rational, you need to provide them with all the information necessary to make their decisions. This calls for omniscient players, which is bad in two ways: first, it creates impractical requirements for information storing and processing of players. Second, game theory is no longer a game theory , as you can replace all players by a central ruler and where is the fun in that?
The value of information in this view is another point of interest. We have already discussed that possessing all the information is infeasible. But what about assuming players with limited knowledge?
Would that help? You may ask anyone involved in this area, but it suffices to say that optimization under uncertainty is tough. Yes, there still are the good-old Nash equilibria. The problem is that they are infinite. Game theory does not provide you with arguments to evaluate them. So, even if you reach one, you shouldn't make it such a big deal. By this point you should suspect that AI applications are much more complicated than the examples classical game theory concerns itself with.
Just to mention a few obstacles on the path of applying the Nash equilibrium approach in a robotic application: imagine being the captain of a team of robots playing football in RoboCup. How fast, strong, and intelligent are your players and your opponents? What strategies does the opponent team use?
How should you reward your players? Clearly, just being familiar with the rules of football will not win you the game. If game theory has been raising debates for decades, if it has been founded on unrealistic assumptions and, for realistic tasks, if it offers complicated and little-understood solutions, why are we still going for it? If we actually understood how groups interact and cooperate to achieve their goals, psychology and politics would be much clearer.
Researchers in the area of multi-agent reinforcement learning either completely emit a discussion on the theoretical properties of their algorithms and nevertheless often exhibit good results or traditionally study the existence of Nash equilibria.
The latter approach seems, to the eyes of a young researcher in the field, like a struggle to prove, under severe, unrealistic assumptions, the theoretical existence of solutions that — being infinite and of questionable value — will never be leveraged in practice. The inception of evolutionary game theory is not recent, yet its far-reaching applications in the area of AI took long to be acknowledged. Originating in biology, it was introduced in , by John M.
To understand game theory power in AI, however, it is essential to understand the basics of what actually constitutes game theory and its applications. The concept gets its name from board games, where strategic interactions are most common. Real-world strategic interactions, however, can be quite complicated.
These complexities can be circumvented by using models, a key aspect in the study of game theory. Some models include the Nash Equilibrium, Pareto Efficiency, the multiplicity of equilibria, Symmetric and Asymmetric and a whole host of other game models. Game theory is relevant to, and applied in almost every sphere of human existence, from war to business, to well, board games. However, in the world of Artificial Intelligence, where machines learn to play for the win, and multidimensional, multi-element interactions are imposed on the models, do things start to get really interesting.
Game theory is a crucial element in building AI models today. Reconfiguring the arms race war debate. Journal of Peace Research 35 1 — Downs, G. Rocke, and P. Is the good news about compliance good news about cooperation?
International Organization — Drezner, D. The sanctions paradox. Egorov, G. The killing game: Reputation and knowledge in politics of succession. Game Theory and Information. Fearon, J. Counterfactuals and hypothesis testing in political science. World Politics 43 January — Domestic political audiences and the escalation of international disputes. Rationalist explanations for war. Feder, S. Factions and policon: New ways to analyze politics.
Westerfield, ed. Forecasting for policy making in the post-cold war period. Annual Review of Political Science — Fey, M. Mutual optimism and war. American Journal of Political Science 51 4 — Gaddis, J. International relations theory and the end of the cold war.
International Security 17 3 — Gartzke, E. War is in the error term. Gibler, D. Rider, and M. Taking arms against a sea of troubles: Conventional arms races during periods of rivalry.
Journal of Peace Research 42 2 — Goemans, H. Which way out: The manner and consequences of losing office. Journal of Conflict Resolution 52 6 — Harsanyi, J. Management Science —, —, — Hufbauer, G. Schott, K. Elliott, and B. Economic sanctions reconsidered , 3rd ed. Huth, P. Extended deterrence and the prevention of war. Standing your ground: Territorial disputes and international conflict. Ann Arbor: University of Michigan Press. What makes deterrence work? Cases from to World Politics — Jones, B.
Hit or miss? The effect of assassinations on institutions and war. American Economic Journal: Macroeconomics 1 2 — Kahneman, D. Choices, values and frames. American Psychologist 39 4 — Norm theory: Comparing reality to its alternatives.
Psychological Review — Karl, T. Dilemmas of democratization in Latin America. Comparative Politics — Kim, W. When do power shifts lead to war? American Journal of Political Science — King, G. Improving forecasts of state failure. World Politics 53 4 — Kydd, A. Trust and mistrust in international relations. Sabotaging the peace: The politics of extremist violence.
International Organization 56 2 — Lalman, D. Alliance formation and national security. International Interactions — Lapan, H. To bargain or not to bargain: That is the question. American Economic Review 78 2 — Terrorism and signaling. European Journal of Political Economy 9 3 — Leeds, B.
Alliance reliability in times of war: Explaining decisions to violate treaties. International Organization 57 Fall — Long, and S. McLaughlin Mitchell. Re-evaluating alliance reliability: Specific threats, specific promises. Journal of Conflict Resolution — Ritter, S. McLaughlin Mitchell, and A. Alliance treaty obligations and provisions, — Martin, L. Coercive cooperation. McDermott, R. Risk-taking in international politics.
Presidential leadership, illness and decision making. Morrow, J. On the theoretical basis of a measure of national risk attitudes. Alliances and asymmetry: An alternative to the capability aggregation model of alliances. Electoral and congressional incentives and arms control. Modeling the forms of cooperation: Distribution versus information. Niou, E. Orde shook, and G. The balance of power. North, D. Wallis, and B. Violence and social orders: A conceptual framework for interpreting recorded human history.
Schmitter, and L. Whitehead, eds. Transitions from authoritarian rule. Risk aversion in international relations theory. Powell, R. Nuclear deterrence theory. In the shadow of power. Ray, J. The future as arbiter of theoretical controversies: Predictions, explanations and the end of the cold war. British Journal of Political Science 26 4 — Riker, W. The strategy of rhetoric. Rosendorff, P. Too much of a good thing? The proactive response dilemma.
Journal of Conflict Resolution 48 4 — Sabrosky, A. Interstate alliances: Their reliability and the expansion of war. Singer, eds. The correlates of War II: Testing some realpolitik models. New York: Free Press. Sargent, T. Bounded rationality in macroeconomics. New York: Oxford University Press. Sartori, A. Deterrence by diplomacy. Schneider, G. Finke, and S. Bargaining power in the European Union: An evaluation of competing game—theoretic model.
Political Studies 58 1 — Schultz, K. Domestic opposition and signaling in international crises. Shepsle, K. Institutional arrangements and equilibrium in multidimensional voting models. Simon, H. Models of man: Social and rational. New York: John Wiley and Sons. Siverson, R. Attributes of national alliance membership and war participation, — American Journal of Political Science 24 1 :1— Slantchev, B.
The power to hurt: Costly conflict with completely informed states. American Political Science Review 97 1 — Smith, A. The success and use of sanctions. International Interactions 21 3 — To intervene or not to intervene: A biased decision. Journal of Conflict Resolution 40 1 — International crises and domestic politics. American Political Science Review 92 3 — Testing theories of strategic choice: The example of crisis escalation.
American Journal of Political Science 93 4 :1,—1, Sonin, K. Dictators and their viziers: Agency problems in dictatorships. Spence, M. Job market signaling. Quarterly Journal of Economics — Taylor, M.
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