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

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

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

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

Expressive latent feature modelling for explainable matrix factorisation-based recommender systems

The traditional matrix factorisation (MF) based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This paper introduces an MF-based explainable recommender system framework that utilises the user-item rating data and the available item information to model meaningful user and item latent features. These features are exploited to enhance the rating prediction accuracy and the recommendation explainability. Our proposed feature-based explainable recommender system framework utilises these meaningful user and item latent features to explain the recommendation without relying on private or outer data. The recommendations are explained to the user using text message and bar chart. Our proposed model has been evaluated in terms of the rating prediction accuracy and the reasonableness of the explanation using six real-world benchmark datasets for movies, books, video games and fashion recommendation systems. The results show that the proposed model can produce accurate explainable recommendations

The negotiation game

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