Finite Model Finding for Parameterized Verification

In this paper we investigate to which extent a very simple and natural "reachability as deducibility" approach, originated in the research in formal methods in security, is applicable to the automated verification of large classes of infinite state and parameterized systems. The approach is based on modeling the reachability between (parameterized) states as deducibility between suitable encodings of states by formulas of first-order predicate logic. The verification of a safety property is reduced to a pure logical problem of finding a countermodel for a first-order formula. The later task is delegated then to the generic automated finite model building procedures. In this paper we first establish the relative completeness of the finite countermodel finding method (FCM) for a class of parameterized linear arrays of finite automata. The method is shown to be at least as powerful as known methods based on monotonic abstraction and symbolic backward reachability. Further, we extend the relative completeness of the approach and show that it can solve all safety verification problems which can be solved by the traditional regular model checking.Comment: 17 pages, slightly different version of the paper is submitted to TACAS 201

The Messenger, Vol. 11, No. 2 & 3 (October, 1904)

https://digitalcommons.bard.edu/messenger/1026/thumbnail.jp

The Compatibility of Zero-Sum Logic and Mutualism in Sport

This essay argues that within competitive sport zero-sum logic and the theory of mutualism are compatible and complementary. Drawing on Robert Simon’s theory of mutualism and Scott Kretchmar’s argument for zero-sum logic, this article shows how athletes can strive for a clear-cut victory and shared benefits such as athletic excellence fully and wholeheartedly at the same time. This paper will also consider how acknowledgment of this dynamic could advance understandings for ethical theories for sport. It will then conclude by describing a subjective approach that will make the affinity of zero-sum logic and mutualism more accessible for sports people

“BLUFF” WITH AI

The goal of this project is to build multiple agents for the game Bluff and to conduct experiments as to which performs better. Bluff is a multi-player, non-deterministic card game where players try to get rid of all the cards in their hand. The process of bluffing involves making a move such that it misleads the opponent and thus prove to be of advantage to the player. The strategic complexity in the game arises due to the imperfect or hidden information which means that certain relevant details about the game are unknown to the players. Multiple agents followed different strategies to compete against each other. Two of the agents tried to play the game in offense mode where they tried to win by removing the cards from the hand efficiently and two other agents in defense mode where they try to prevent or delay other players from winning by calling Bluff on them when they have few cards left. In the experiments that we conducted with all four agents competing against each other, we found that the best strategy was to not Bluff and play truthfully. Playing the right cards, gave the most wins to any player. Also we found out that calling Bluff on a player even if we have more than one card of the same rank would prove risky, since there is a chance that the player was actually playing the correct cards and we could lose the bet as shown by the Anxious AI. We conducted an interesting experiment to find out the best defense strategy and which agent would catch the most number of bluffs correctly. The Anxious AI was the winner. We also try to “teach” an agent how to play the game effectively and experiments show that the agent did learn the strategy very well. We also found that the Smart AI was the evolutionary stable strategy among the four agents