This document ("ECOLANG_v_1_3c_Eng.doc") describes the communication language used in one multi-agent systems environment for ecological simulations, based on the EcoDynamo simulator application (Pereira and Duarte 2005) linked with several intelligent agents and visualisation applications and extends the initial definition of the language (Pereira et al. 2005). The agents' actions and perceptions are translated into messages exchanged with the simulator application and other agents. The concepts' definitions used follow the BNF notation (Backus et al. 1960) and it's inspired in the Coach Unilang language (Reis and Lau 2002). ECOLANG notation is an extension to the original BNF formalism adding the following meta-symbols: { } used for repetitive items (one or more times); [ ] encloses types of values; Terminal symbols use bold face letters.

- In every mathematical model, parameters regulate the behaviour of equations describing temporal and spatial changes of model state variables and their interactions. Generally, there is some uncertainty associated with each parameter. Model calibration is performed by comparing observed with predicted data and it is a crucial phase in the modelling process. It's an iterative and interactive task in which, after each simulation, the "modeller" analyses the results and performs changes on one or more equation's parameters trying to tune the model. This "tuning" procedure is a hard and "tedious" work requiring a good understanding of the effects of different parameters over the available variables. Automatic calibration procedures, based on systematic and exhaustive generation of parameter vectors and using several convergence methods, are available but they require a large number of model runs and are, therefore, not applicable to very complex ecosystem models demanding large computational times.

Multiagent systems ([10], [31], [33], [26]) are an increasingly important and active area of interdisciplinary research on the border of computer science, artificial intelligence, and game theory, as they model a wide variety of phenomena in these fields, including open and interactive systems, distributed computations, security protocols, knowledge and information exchange, coalitional abilities in games, etc. Not surprisingly, a number of logical formalisms have been proposed for specification, verification, and reasoning about multiagent systems.

These group interactions can appear to an outside observer to mimic the intelligence of a single entity. In social sciences some prominent theorists have discussed emergence - perhaps without even realising it. In all events, many social phenomena can be modeled using emergence. This paper discusses emergence as a tool of political analysis, examines existing political simulations, and proposes a simple simulation using an agent model to determine emergent characteristics of a political system. The paper is divided into three sections: First, a description of emergence in social science.

Just as war is sometimes fallaciously represented as a zero sum game -- when in fact war is a negative sum game - stock market trading, a positive sum game over time, is often erroneously represented as a zero sum game. This is called the "zero sum fallacy" -- the erroneous belief that one trader in a stock market exchange can only improve their position provided some other trader's position deteriorates. However, a positive sum game in absolute terms can be recast as a zero sum game in relative terms. Similarly it appears that negative sum games in absolute terms have been recast as zero sum games in relative terms: otherwise, why would zero sum games be used to represent situations of war? Such recasting may have heuristic or pedagogic interest but recasting must be clearly explicited or risks generating confusion. Keywords: Game theory, stock trading and agent based AI.

We derive optimal strategies for a bidding agent that participates in multiple, simultaneous second-price auctions with perfect substitutes. We prove that, if everyone else bids locally in a single auction, the global bidder should always place non-zero bids in all available auctions, provided there are no budget constraints. With a budget, however, the optimal strategy is to bid locally if this budget is equal or less than the valuation. Furthermore, for a wide range of valuation distributions, we prove that the problem of finding the optimal bids reduces to two dimensions if all auctions are identical. Finally, we address markets with both sequential and simultaneous auctions, non-identical auctions, and the allocative efficiency of the market.

In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem as a distributed constraint optimization problem (DCOP), in which each agent can communicate with other agents that share an interest in one or more variables. Whereas existing DCOP algorithms can be easily manipulated by an agent, either by misreporting private information or deviating from the algorithm, we introduce M-DPOP, the first DCOP algorithm that provides a faithful distributed implementation for efficient social choice. This provides a concrete example of how the methods of mechanism design can be unified with those of distributed optimization. Faithfulness ensures that no agent can benefit by unilaterally deviating from any aspect of the protocol, neither information-revelation, computation, nor communication, and whatever the private information of other agents. We allow for payments by agents to a central bank, which is the only central authoritythat we require. To achieve faithfulness, we carefully integrate the Vickrey-Clarke-Groves (VCG) mechanism with the DPOP algorithm, such that each agent is only asked to perform computation, report information, and send messages that is in its own best interest. Determining agent i's payment requires solving the social choice problem without agent i. Here, we present a method to reuse computation performed in solving the main problem in a way that is robust against manipulation by the excluded agent. Experimental results on structured problems show that as much as 87% of the computation required for solving the marginal problems can be avoided by re-use, providing very good scalability in the number of agents. On unstructured problems, we observe a sensitivity of M-DPOP to the density of the problem, and we show that reusability decreases from almost 100% for very sparse problems to around 20% for highly connected problems. We close with a discussion of the features of DCOP that enable faithful implementations in this problem, the challenge of reusing computation from the main problem to marginal problems in other algorithms such as ADOPT and OptAPO, and the prospect of methods to avoid the welfare loss that can occur because of the transfer of payments to the bank.

The peer-review process is the most widely accepted certification mechanism for officially accepting the written results of researchers within the scientific community. An essential component of peer-review is the identification of competent referees to review a submitted manuscript. This article presents an algorithm to automatically determine the most appropriate reviewers for a manuscript by way of a co-authorship network data structure and a relative-rank particle-swarm algorithm. This approach is novel in that it is not limited to a pre-selected set of referees, is computationally efficient, requires no human-intervention, and, in some instances, can automatically identify conflict of interest situations. A useful application of this algorithm would be to open commentary peer-review systems because it provides a weighting for each referee with respects to their expertise in the domain of a manuscript. The algorithm is validated using referee bid data from the 2005 Joint Conference on Digital Libraries.

Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis.

We consider the problem of allocating fairly a set of indivisible goods among agents from the point of view of compact representation and computational complexity. We start by assuming that agents have dichotomous preferences expressed by propositional formulae. We express efficiency and envy-freeness in a logical setting, which reveals unexpected connections to nonmonotonic reasoning. Then we identify the complexity of determining whether there exists an efficient and envy-free allocation, for several notions of efficiency, when preferences are represented in a succinct way (as well as restrictions of this problem). We first study the problem under the assumption that preferences are dichotomous, and then in the general case.