Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

/pdf/hyper-heuristics-a-survey-of-the-state-of-the-art-mwp7dhxfb3.pdf

Hyper-heuristics: a survey of the state of the art

(2002). Getting to Yes: Negotiating Agreement without Giving in. Quality Management Journal: Vol. 9, No. 2, pp. 73-74.

Getting to Yes: Negotiating Agreement without Giving in

The Art and Science of Negotiation

https://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/aij2004a.pdf

An agenda-based framework for multi-issue negotiation

https://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/aij2008.pdf

A linear approximation method for the Shapley value

This paper presents a new model for multi-issue negotiation under time constraints in an incomplete information setting. In this model the order in which issues are bargained over and agreements are reached is determined endogenously as part of the bargaining equilibrium. We show that the sequential implementation of the equilibrium agreement gives a better outcome than a simultaneous implementation when agents have like, as well as conflicting, time preferences. We also show that the equilibrium solution possesses the properties of uniqueness and symmetry, although it is not always Pareto-optimal.

/pdf/multi-issue-negotiation-under-time-constraints-1lgz9jc0u7.pdf

Multi-issue negotiation under time constraints

This paper studies bilateral multi-issue negotiation between self-interested autonomous agents. Now, there are a number of different procedures that can be used for this process; the three main ones being the package deal procedure in which all the issues are bundled and discussed together, the simultaneous procedure in which the issues are discussed simultaneously but independently of each other, and the sequential procedure in which the issues are discussed one after another. Since each of them yields a different outcome, a key problem is to decide which one to use in which circumstances. Specifically, we consider this question for a model in which the agents have time constraints (in the form of both deadlines and discount factors) and information uncertainty (in that the agents do not know the opponent's utility function). For this model, we consider issues that are both independent and those that are interdependent and determine equilibria for each case for each procedure. In so doing, we show that the package deal is in fact the optimal procedure for each party. We then go on to show that, although the package deal may be computationally more complex than the other two procedures, it generates Pareto optimal outcomes (unlike the other two), it has similar earliest and latest possible times of agreement to the simultaneous procedure (which is better than the sequential procedure), and that it (like the other two procedures) generates a unique outcome only under certain conditions (which we define).

/pdf/multi-issue-negotiation-with-deadlines-1aodqhgaji.pdf

Multi-issue negotiation with deadlines

This paper analyzes the process of automated negotiation between two competitive agents that have firm deadlines and incomplete information about their opponent. Generally speaking, the outcome of a negotiation depends on many parameters-including the agents' preferences, their reservation limits, their attitude toward time and the strategies they use. Although in most realistic situations it is not possible for agents to have complete information about each of these parameters for its opponent, it is not uncommon for agents to have partial information about some of them. Under such uncertainty, our aim is to determine how an agent can exploit its available information to select an optimal strategy. Here, in particular, the optimal strategies are determined considering all possible ways in which time can effect negotiation. Moreover, we list the conditions for convergence when both agents use their respective optimal strategies and study the effect of time on negotiation outcome.

Shaheen Fatima

Papers

An agenda-based framework for multi-issue negotiation

A linear approximation method for the Shapley value

Multi-issue negotiation under time constraints

Multi-issue negotiation with deadlines

Optimal negotiation strategies for agents with incomplete information