19 research outputs found
Multilateral bargaining for resource division
We address the problem of how a group of agents can
decide to share a resource, represented as a unit-sized pie. 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 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), all the players get nothing. 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 agreement, and, under certain conditions provides a
foundation for the core of the Bayesian voting game. Our analysis
also 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 a non-cooperative equilibrium becomes NP-hard even
under the perfect information assumption
Majority bargaining for resource division
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
Heuristic methods for optimal coalition structure generation
The problem of finding the optimal coalition structure arises frequently in multiagent systems. Heuristic approaches for solving this problem are needed because of its computational complexity. This paper studies two such approaches: tabu search and simulated annealing. Through simulations we show that tabu search generates better quality solutions than simulated annealing for coalition games in characteristic function form and those in partition function form
Bargaining for coalition structure formation
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
Power and welfare in bargaining for coalition structure formation
We investigate a noncooperative bargaining game for partitioning n agents into non-overlapping coalitions. The game has n time periods during which the players are called according to an exogenous agenda to propose offers. With probability δ, the game ends during any time period t< n. If it does, the first t players on the agenda get a chance to propose but the others do not. Thus, δ is a measure of the degree of democracy within the game (ranging from democracy for δ= 0 , through increasing levels of authoritarianism as δ approaches 1, to dictatorship for δ= 1). We determine the subgame perfect equilibrium (SPE) and study how a player’s position on the agenda affects his bargaining power. We analyze the relation between the distribution of power of individual players, the level of democracy, and the welfare efficiency of the game. We find that purely democratic games are welfare inefficient and that introducing a degree of authoritarianism into the game makes the distribution of power more equitable and also maximizes welfare. These results remain invariant under two types of player preferences: one where each player’s preference is a total order on the space of possible coalition structures and the other where each player either likes or dislikes a coalition structure. Finally, we show that the SPE partition may or may not be core stable
Cross organisational compatible plans generation framework
In this modern era, organisations have to work in coordination with
many other organisations in order to succeed in business. Interacting organisations
can only proceed in business if they have compatible workflows. This paper proposes
a framework to automatically generate compatible workflows for multiple
interacting organisations from their process definitions and service descriptions.
Existing systems can reconcile existing workflows only, and cannot generate
compatible workflows for multiple organisations automatically. The proposed
system is different from existing systems since it targets workflow collaboration
by generating workflows automatically. This allows the organisations to save the
time that would otherwise be spent in modelling workflows and making them
compatible with the workflows of interacting organisations
A cross organisation compatible workflows generation and execution framework
With the development of the Internet, the demand for electronic and online commerce has increased. This has, in turn, increased the demand for business process automation. In this paper, we look at the use of workflows for business process automation. An automatically generated workflow can save time and resources needed for running online businesses. In general, due to the interdependencies between their activities, multiple business organisations will need to work together by collaborating and coordinating their activities with each other. This gives rise to the need for workflow collaboration across organisations. Current systems for workflow collaboration are only capable of reconciling existing workflows of the collaborating organisations. Automatic workflow generation systems only generate workflows for individual organisations and cannot handle the automatic generation of compatible workflows for multiple collaborating organisations. To overcome this problem, in this paper, we present a framework that is able to generate multiple sets of compatible workflows for multiple collaborating organisations. The proposed framework supports runtime enactment and runtime collaboration of the generated workflows. This framework enables users to save the time and resources that would otherwise be spent in modelling, reconciling and reengineering workflows
Augmenting reinforcement learning to enhance cooperation in the iterated prisoner’s dilemma
Reinforcement learning algorithms applied to social dilemmas sometimes struggle with converging to mutual cooperation against like-minded partners, particularly when utilising greedy behavioural selection methods. Recent research has demonstrated how affective cognitive mechanisms, such as mood and emotion, might facilitate increased rates of mutual cooperation when integrated with these algorithms. This research has, thus far, primarily utilised mobile multi-agent frameworks to demonstrate this relationship - where they have also identified interaction structure as a key determinant of the emergence of cooperation. Here, we use a deterministic, static interaction structure to provide deeper insight into how a particular moody reinforcement learner might encourage the evolution of cooperation in the Iterated Prisoner’s Dilemma. In a novel grid environment, we both replicated original test parameters and then varied the distribution of agents and the payoff matrix. We found that behavioural trends from past research were present (with suppressed magnitude), and that the proportion of mutual cooperations was heightened when both the influence of mood and the cooperation index of the payoff matrix chosen increased. Changing the proportion of moody agents in the environment only increased mutual cooperations by virtue of introducing cooperative agents to each other
Multi-agent recommender system
A recommender agent (RA) provides users with
recommendations about products/ services. Recommendations are made on the basis of information
available about the products/ services and the users,
and this process typically involves making predictions about user preferences and matching them
with product attributes. Machine learning methods are being studied extensively to design RAs.
In this approach, a model is learnt from historical data about trading (i.e. data about products
and the users buying them). There are numerous
different learning methods, and how accurately a
method can make a recommendation depends on
the method and also on the type of historical data.
Given this, we propose a multi-agent recommender
system called MARS which combines various different machine learning methods. Within MARS,
different agents are designed to make recommendations using different machine learning methods.
Since different agents use different machine learning methods, the recommendations they make may
be conflicting. Negotiation is used to come to an
agreement on a recommendation. Negotiation is
conducted using a contract-net protocol. The performance of MARS is evaluated in terms of recommendation error. The results of simulations show
that MARS outperforms five existing recommender
systems
Optimal coalition structures for probabilistically monotone partition function games
For cooperative games with externalities, the problem of optimally partitioning a set of players into disjoint exhaustive coalitions is called coalition structure generation, and is a fundamental computational problem in multi-agent systems. Coalition structure generation is, in general, computationally hard and a large body of work has therefore investigated the development of efficient solutions for this problem. However, the existing methods are mostly limited to deterministic environments. In this paper, we focus attention on uncertain environments. Specifically, we define probabilistically monotone partition function games, a subclass of the well-known partition function games in which we introduce uncertainty. We provide a constructive proof that an exact optimum can be found using a greedy approach, present an algorithm for finding an optimum, and analyze its time complexity.</p