By F. Nielson, P. Cousot, M. Dam, P. Degano (auth.), Mads Dam (eds.)
This ebook originates from the fifth LOMAPS Workshop on research and Verification of Multiple-Agent Languages, held in Stockholm, Sweden, in June 1996. LOMAPS is an ESPRIT undertaking dedicated to software research and verification suggestions acceptable to rising multi-paradigm programming languages.
The quantity provides 14 revised complete papers chosen from the workshop submissions including four invited contributions; additionally incorporated is an introductory assessment surveying the state-of-the-art within the quarter and placing the contributions into this context.
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Extra resources for Analysis and Verification of Multiple-Agent Languages: 5th LOMAPS Workshop Stockholm, Sweden, June 24–26, 1996 Selected Papers
Depending on α and β, the update scheme is referred to as linear reward-penalty (LR−P ) if α = β, for β = 0 it is called linear reward-inaction (LR−I ), and if β is chosen to be small compared to α it is called linear reward- -penalty (LR− P ). Assuming that r is continuous (called S-model ) and in the range [0, 1], (2) does indeed give a probability distribution satisfying the following two conk πi (t + 1) = 1 and ∀i πi (t + 1) ∈ [0, 1]. straints: i=1 Parameterized Learning Automata. A learning automaton following the update rule given in (2) is only guaranteed to converge locally .
D C D 3,3 0,5 C 5,0 1,1 Matching Pennies (MP) is a 2 x 2 game belonging to category (b) and deﬁned by the following payoﬀ matrix: H T H 1,-1 -1, 1 T -1, 1 1,-1 Multi-agent Learning Dynamics: A Survey 43 Both players chose simultaneously for one side of a penny, either they play Head (H) or Tail (T ). If both pennies show the same face player 1 keeps the coins; for a mismatch player 2 gets rewarded. 5. Category (c) is covered by the next example, the Bach or Stravinsky (BoS) game, also referred to as the Battle of the Sexes.
Consider the example of learning in soccer as in . In the initial phase of learning both agents lack the skill for good actions, so even a perfect forward pass may frequently be not rewarded. This leads to the agents converging to actions that work well with a variety of opponents’ strategies but it often results in suboptimal behavior. In order to handle this problem lenient Q-learning collects L rewards for an action before it updates the estimation based on the maximum. Lower rewards are discarded and only the highest reward is used for the update which implies that only L1 · iterations learning steps are executed.
Analysis and Verification of Multiple-Agent Languages: 5th LOMAPS Workshop Stockholm, Sweden, June 24–26, 1996 Selected Papers by F. Nielson, P. Cousot, M. Dam, P. Degano (auth.), Mads Dam (eds.)