On Games with Imperfect Recall and Games with Perfect Recall

Author(s):  
Dionysius Glycopantis
Author(s):  
Jiri Cermak ◽  
Branislav Bošanský ◽  
Viliam Lisý

We solve large two-player zero-sum extensive-form games with perfect recall. We propose a new algorithm based on fictitious play that significantly reduces memory requirements for storing average strategies. The key feature is exploiting imperfect recall abstractions while preserving the convergence rate and guarantees of fictitious play applied directly to the perfect recall game. The algorithm creates a coarse imperfect recall abstraction of the perfect recall game and automatically refines its information set structure only where the imperfect recall might cause problems. Experimental evaluation shows that our novel algorithm is able to solve a simplified poker game with 7.10^5 information sets using an abstracted game with only 1.8% of information sets of the original game. Additional experiments on poker and randomly generated games suggest that the relative size of the abstraction decreases as the size of the solved games increases.


Author(s):  
Francesco Belardinelli ◽  
Alessio Lomuscio ◽  
Aniello Murano ◽  
Sasha Rubin

We develop a logic-based technique to analyse finite interactions in multi-agent systems. We introduce a semantics for Alternating-time Temporal Logic (for both perfect and imperfect recall) and its branching-time fragments in which paths are finite instead of infinite.  We study validities of these logics and present optimal algorithms for their model-checking problems in the perfect recall case.


2009 ◽  
Vol 2009 ◽  
pp. 1-14 ◽  
Author(s):  
María Elena Acevedo ◽  
Marco Antonio Acevedo ◽  
Federico Felipe

Bidirectional Associative Memories (BAMs) based on first model proposed by Kosko do not have perfect recall of training set, and their algorithm must iterate until it reaches a stable state. In this work, we use the model of Alpha-Beta BAM to classify automatically cancer recurrence in female patients with a previous breast cancer surgery. Alpha-Beta BAM presents perfect recall of all the training patterns and it has a one-shot algorithm; these advantages make to Alpha-Beta BAM a suitable tool for classification. We use data from Haberman database, and leave-one-out algorithm was applied to analyze the performance of our model as classifier. We obtain a percentage of classification of 99.98%.


2019 ◽  
Vol 66 ◽  
pp. 197-223
Author(s):  
Michal Jozef Knapik ◽  
Etienne Andre ◽  
Laure Petrucci ◽  
Wojciech Jamroga ◽  
Wojciech Penczek

In this paper we investigate the Timed Alternating-Time Temporal Logic (TATL), a discrete-time extension of ATL. In particular, we propose, systematize, and further study semantic variants of TATL, based on different notions of a strategy. The notions are derived from different assumptions about the agents’ memory and observational capabilities, and range from timed perfect recall to untimed memoryless plans. We also introduce a new semantics based on counting the number of visits to locations during the play. We show that all the semantics, except for the untimed memoryless one, are equivalent when punctuality constraints are not allowed in the formulae. In fact, abilities in all those notions of a strategy collapse to the “counting” semantics with only two actions allowed per location. On the other hand, this simple pattern does not extend to the full TATL. As a consequence, we establish a hierarchy of TATL semantics, based on the expressivity of the underlying strategies, and we show when some of the semantics coincide. In particular, we prove that more compact representations are possible for a reasonable subset of TATL specifications, which should improve the efficiency of model checking and strategy synthesis.


2020 ◽  
Vol 282 ◽  
pp. 103248
Author(s):  
Jiří Čermák ◽  
Viliam Lisý ◽  
Branislav Bošanský

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