The challenge of poker Billings, Darse; Davidson, Aaron; Schaeffer, Jonathan ...
Artificial intelligence,
2002, 2002-01-00, 20020101, Letnik:
134, Številka:
1
Journal Article
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Poker is an interesting test-bed for artificial intelligence research. It is a game of imperfect information, where multiple competing agents must deal with probabilistic knowledge, risk assessment, ...and possible deception, not unlike decisions made in the real world. Opponent modeling is another difficult problem in decision-making applications, and it is essential to achieving high performance in poker.
This paper describes the design considerations and architecture of the poker program
Poki. In addition to methods for hand evaluation and betting strategy,
Poki uses learning techniques to construct statistical models of each opponent, and dynamically adapts to exploit observed patterns and tendencies. The result is a program capable of playing reasonably strong poker, but there remains considerable research to be done to play at a world-class level.
A complex and challenging bilateral negotiation environment for rational autonomous agents is where agents negotiate multi-issue contracts in unknown application domains with unknown opponents under ...real-time constraints. In this paper we present a negotiation strategy called EMAR for this kind of environment that relies on a combination of Empirical Mode Decomposition (EM̲D) and Autoregressive Moving Average (AR̲MA). EMAR enables a negotiating agent to acquire an opponent model and to use this model for adjusting its target utility in real-time on the basis of an adaptive concession-making mechanism. Experimental results show that EMAR outperforms best performing agents from the recent Automated Negotiating Agents Competitions (ANAC) in a wide range of application domains. Moreover, an analysis based on empirical game theory is provided that shows the robustness of EMAR in different negotiation contexts.
Many negotiations in the real world are characterized by incomplete information, and participants’ success depends on their ability to reveal information in a way that facilitates agreements without ...compromising their individual gain. This paper presents an agent-design that is able to negotiate proficiently with people in settings in which agents can choose to truthfully reveal their private information before engaging in multiple rounds of negotiation. Such settings are analogous to real-world situations in which people need to decide whether to disclose information such as when negotiating over health plans and business transactions. The agent combined a decision-theoretic approach with traditional machine-learning techniques to reason about the social factors that affect the players’ revelation decisions on people’s negotiation behavior. It was shown to outperform people as well as agents playing the equilibrium strategy of the game in empirical studies spanning hundreds of subjects. It was also more likely to reach agreement than people or agents playing equilibrium strategies. In addition, it had a positive effect on people’s play, allowing them to reach significantly better performance when compared to people’s play with other people. These results are shown to generalize for two different settings that varied how players depend on each other in the negotiation.
Multi-agent systems are broadly known for being able to simulate real-life situations which require the interaction and cooperation of individuals. Opponent modeling can be used along with ...multi-agent systems to model complex situations such as competitions like soccer games. In this study, a model for predicting opponent moves based on their target is presented. The model is composed by an offline step (learning phase) and an online one (execution phase). The offline step gets and analyses previous experiences while the online step uses the data generated by offline analysis to predict opponent moves. This model is illustrated by an experiment with the Rob degree Cup 2D Soccer Simulator. The proposed model was tested using 22 games to create the knowledge base and getting an accuracy rate over 80%.
People model other people's mental states in order to understand and predict their behavior. Sometimes they model what others think about them as well: ＂He thinks that I intend to stop.＂ Such ...second-order theory of mind is needed to navigate some social situations, for example, to make optimal decisions in turn-taking games. Adults sometimes find this very difficult. Sometimes they make decisions that do not fit their predictions about the other player. However, the main bottleneck for decision makers is to take a second-order perspective required to make a correct opponent model. We report a methodical investigation into supporting factors that help adults do better. We presented subjects with two-player, three-turn games in which optimal decisions required second-order theory of mind (Hedden and Zhang, 2002). We applied three ＂scaffolds＂ that, theoretically, should facilitate second-order perspective-taking: 1) stepwise training, from simple one-person games to games requiring second-order theory of mind; 2) prompting subjects to predict the opponent's next decision before making their own decision; and 3) a realistic visual task representation. The performance of subjects in the eight resulting combinations shows that stepwise training, but not the other two scaffolds, improves subjects' second-order opponent models and thereby their own decisions.
Celotno besedilo
Dostopno za:
CEKLJ, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Automated negotiation among self-interested autonomous agents has gained tremendous attention due to the diversity of its broad range of potential real-world applications. This article deals with a ...prominent type of such negotiations, namely, multiissue negotiation that runs under continuous-time constraints and in which the negotiating agents have no prior knowledge about their opponents’ preferences and strategies. A negotiation strategy called
Dragon
is described that employs sparse pseudoinput Gaussian processes. Specifically,
Dragon
enables an agent (1) to precisely model the behavior of its opponents with comparably low computational load and (2) to make decisions effectively and adaptively in very complex negotiation settings. Extensive experimental results, based on a number of negotiation scenarios and state-of-the-art negotiating agents from Automated Negotiating Agents Competitions, are provided. Moreover, the robustness of our strategy is evaluated through both empirical game-theoretic and spatial evolutionary game-theoretic analysis.
Electronic negotiation systems can incorporate computational models and algorithms in order to help negotiators achieve their objectives. An important opportunity in this respect is the development ...of a component, which can assess an expected reaction by a counterpart to a given trial offer before it is submitted. This work proposes a pairwise modeling approach that provides the possibility of developing flexible and generic models for counteroffer prediction when the negotiation cases are similar. The key feature is that each negotiated issue is predicted while paired with each of the other issues and the permutations of issue pairs across all negotiation offers are confounded together. This data fusion permits extractions of common relationships across all issues, resulting in a type of pattern fusion. Experiments with electronic negotiation data demonstrated that the model's predictive performance is equivalent to case-specific models while offering a high degree of flexibility and generality even when predicting to a new issue.
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