DSpace Community:
https://dspace.lboro.ac.uk/2134/83
2015-02-25T10:55:22ZMajority bargaining for resource division
https://dspace.lboro.ac.uk/2134/16733
Title: Majority bargaining for resource division
Authors: Fatima, Shaheen; Wooldridge, Michael
Abstract: We address the problem of how a set of agents can decide to share a resource,
represented as a unit-sized pie. The pie can be generated by the entire set but
also by some of its subsets. We investigate a finite horizon non-cooperative bargaining
game, in which the players take it in turns to make proposals on how the resource
should for this purpose be allocated, and the other players vote on whether or not to
accept the allocation. Voting is modelled as a Bayesian weighted voting game with
uncertainty about the players’ weights. The agenda, (i.e., the order in which the players
are called to make offers), is defined exogenously. We focus on impatient players
with heterogeneous discount factors. In the case of a conflict, (i.e., no agreement by
the deadline), no player receives anything. We provide a Bayesian subgame perfect
equilibrium for the bargaining game and conduct an ex-ante analysis of the resulting
outcome. We show that the equilibrium is unique, computable in polynomial time,
results in an instant Pareto optimal outcome, and, under certain conditions provides
a foundation for the core and also the nucleolus of the Bayesian voting game. In
addition, our analysis leads to insights on how an individual’s bargained share is in-
fluenced by his position on the agenda. Finally, we show that, if the conflict point of
the bargaining game changes, then the problem of determining the non-cooperative
equilibrium becomes NP-hard even under the perfect information assumption. Our
research also reveals how this change in conflict point impacts on the above mentioned
results.
Description: Closed access2015-05-01T00:00:00ZTime-series event-based prediction: an unsupervised learning framework based on genetic programming
https://dspace.lboro.ac.uk/2134/16730
Title: Time-series event-based prediction: an unsupervised learning framework based on genetic programming
Authors: Kattan, Ahmed; Fatima, Shaheen; Arif, Muhammad
Abstract: In this paper, we propose an unsupervised learning framework based on Genetic Programming
(GP) to predict the position of any particular target event (defined by the user) in a
time-series. GP is used to automatically build a library of candidate temporal features.
The proposed framework receives a training set S ¼ fðVaÞja ¼ 0 ng, where each Va is a
time-series vector such that 8Va 2 S; Va ¼ fðxtÞjt ¼ 0 tmaxg where tmax is the size of the
time-series. All Va 2 S are assumed to be generated from the same environment. The proposed
framework uses a divide-and-conquer strategy for the training phase. The training
process of the proposed framework works as follow. The user specifies the target event that
needs to be predicted (e.g., Highest value, Second Highest value, ..., etc.). Then, the framework
classifies the training samples into different Bins, where Bins ¼ fðbiÞji ¼ 0 tmaxg,
based on the time-slot t of the target event in each Va training sample. Each bi 2 Bins will
contain a subset of S. For each bi, the proposed framework further classifies its samples into
statistically independent clusters. To achieve this, each bi is treated as an independent
problem where GP is used to evolve programs to extract statistical features from each
bi’s members and classify them into different clusters using the K-Means algorithm. At
the end of the training process, GP is used to build an ‘event detector’ that receives an
unseen time-series and predicts the time-slot where the target event is expected to occur.
Empirical evidence on artificially generated data and real-world data shows that the proposed
framework significantly outperforms standard Radial Basis Function Networks, standard
GP system, Gaussian Process regression, Linear regression, and Polynomial Regression.
Description: Closed access2015-01-01T00:00:00ZThe negotiation game
https://dspace.lboro.ac.uk/2134/16729
Title: The negotiation game
Authors: Fatima, Shaheen; Kraus, Sarit; Wooldridge, Michael
Abstract: In this paper, the authors consider some of the main ideas underpinning attempts to build automated negotiators--computer programs that can effectively negotiate on our behalf. If we want to build programs that will negotiate on our behalf in some domain, then we must first define the negotiation domain and the negotiation protocol. Defining the negotiation domain simply means identifying the space of possible agreements that could be acceptable in practice. The negotiation protocol then defines the rules under which negotiation will proceed, including a rule that determines when agreement has been reached, and what will happen if the participants fail to reach agreement. One important insight is that we can view negotiation as a game, in the sense of game theory: for any given negotiation domain and protocol, negotiating agents have available to them a range of different negotiation strategies, which will result in different outcomes, and hence different benefits to them. An agent will desire to choose a negotiation strategy that will yield the best outcome for itself, but must take into account that other agents will be trying to do the same.
Description: Closed access.2014-01-01T00:00:00ZBargaining for coalition structure formation
https://dspace.lboro.ac.uk/2134/16727
Title: Bargaining for coalition structure formation
Authors: Fatima, Shaheen; Michalak, Tomasz; Wooldridge, Michael
Abstract: Many multiagent settings require a collection of agents
to partition themselves into coalitions. In such cases, the agents may
have conflicting preferences over the possible coalition structures
that may form. We investigate a noncooperative bargaining game to
allow the agents to resolve such conflicts and partition themselves
into non-overlapping coalitions. The game has a finite horizon and
is played over discrete time periods. The bargaining agenda is de-
fined exogenously. An important element of the game is a parameter
0 ≤ δ ≤ 1 that represents the probability that bargaining ends
in a given round. Thus, δ is a measure of the degree of democracy
(ranging from democracy for δ = 0, through increasing levels of
authoritarianism as δ approaches 1, to dictatorship for δ = 1). For
this game, we focus on the question of how a player’s position on the
agenda affects his power. We also analyse the relation between the
distribution of the power of individual players, the level of democracy,
and the welfare efficiency of the game. Surprisingly, we find
that purely democratic games are welfare inefficient due to an uneven
distribution of power among the individual players. Interestingly,
introducing a degree of authoritarianism into the game makes
the distribution of power more equitable and maximizes welfare.
Description: This is a conference paper from ECAI 2014. It is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License.2014-01-01T00:00:00Z