New Zealand

Septimus van der Linden (June 1st 2010). Wind Power: Integrating Wind Turbine Generators (WTG’s) with Energy Storage, Wind Power, S M Muyeen, IntechOpen, DOI: 10.5772/8354. 

Sergio Faias, Jorge Sousa and Rui Castro (December 1st 2009). Embedded Energy Storage Systems in the Power Grid for Renewable Energy Sources Integration, Renewable Energy, T J Hammons, IntechOpen, DOI: 10.5772/7376. 

T. J. Hammons (June 8th 2011). Europe: Impact of Dispersed and Renewable Generation on Power System Structure, Electricity Infrastructures in the Global Marketplace, T. J. Hammons, IntechOpen, DOI: 10.5772/37846.

Deregulated power systems with high renewable penetration often involve complex decision-making by self-interested private investors. In this work, we study the setting of privately developed and shared network capacity, where the power grid infrastructure, renewable generation and storage units are built by profit-driven investors. Specifically, we consider a case where demand and generation sites are not co-located, and a private investor installs generation capacity and a power line between the two locations providing also access to rival competitors (local generators and storage investors) against a fee. We show such a setting leads to a bilevel Stackelberg-Cournot game between the line investor (leader) and local investors (followers) and develop a data-driven solution to derive the profit-maximising capacities installed by players at equilibrium, based on analysis of a large-scale empirical dataset from a grid upgrade project in the UK. Our method provides a realistic tool to analyse decision-making of private investors in such games and subsequently encourage further adoption of renewable generation.

AAMAS 2020

Strategic Decision-Making for Power Network Investments With Distributed Renewable Generation

stackelberg-cournot game

renewable generation

network expansion

energy storage

game theory

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

technical paper

AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). The aim of the joint conference is to provide a single, high-profile, internationally-respected archival forum for scientific research in the theory and practice of autonomous agents and multi-agent systems.

Browse keynotes, discussions, panels and over 300 presentations.


AAMAS is the leading scientific conference for research in autonomous agents and multi-agent systems. The AAMAS conference series was initiated in 2002 as the merging of three respected scientific meetings: the International Conference on Multi-Agent Systems (ICMAS), the International Workshop on Agent Theories, Architectures, and Languages (ATAL), and the International Conference on Autonomous Agents (AA). 

Social interactions are key in multi-agent systems. Social dilemmas have been widely studied to model specific problems in social interactions. However, state-of-the-art social dilemmas have disregarded specific ethical aspects affecting interactions.

Here we propose a novel model for social dilemmas, the so-called Sequential Moral Dilemmas, that do capture the notion of moral value. First, we provide a formal definition of sequential moral dilemmas as Markov Games. Thereafter, we formally characterise theoretically the conditions for agents to learn to behave ethically, so that they are aligned with the moral value. Moreover, we exploit our characterisation to provide a structural solution to a sequential moral dilemma, namely how to configure the Markov game to solve the dilemma. 

Finally, we illustrate our proposal through the so-called public civility game, an example of a sequential moral dilemma considering the civility value. We show the social benefits obtained when the agents learn to adhere to the moral value.

A Structural Solution to Sequential Moral Dilemmas

BDI logics, i.e., logics with belief, desire and intention attitudes, are one of the most widely studied formal languages for modelling rational agents. 
In this paper, we consider the logic CTL∗_BDI that augments the branching-time logic CTL∗ with the BDI modalities and adopt the possible-world semantics by Rao and Georgeff. We recall that in this semantics bdi relations vary over time according to a branching-time structure. We study the related model-checking question for finite-state structures, and in particular, we focus on models that are described as tuples of Kripke structures (one for
each world) and where the BDI relations are captured by finite-state relations. Note that for formulas that do not contain BDI modalities this corresponds to standard CTL∗ model-checking that is known to be PSPACE-complete. We show that by adding the BDI modalities
the computational complexity of model-checking remains PSPACE-complete. 
The problem is still PSPACE-hard even if we disallow the nesting of temporal operators in the path formulas, i.e., we restrict to the temporal modalities of CTL. 
Finally, we give a fixed-point formulation of our algorithm for Ctlbdi that implements it on the top of existing symbolic fixed-point solvers.

On the Model-Checking of Branching-time Temporal Logic with BDI Modalities

Bidding and acceptance strategies have a substantial impact on the outcome of negotiations in scenarios with linear additive and nonlinear utility functions. Over the years, it has become clear that there is no single best strategy for all negotiation settings, yet many fixed strategies are still being developed. We envision a shift in the strategy design question from: What is a good strategy?, towards: What could be a good strategy? For this purpose, we developed a method leveraging automated algorithm configuration to find the best strategies for a specific set of negotiation settings. By empowering automated negotiating agents using automated algorithm configuration, we obtain a flexible negotiation agent that can be configured automatically for a rich space of opponents and negotiation scenarios.
To critically assess our approach, the agent was tested in an ANAC-like bilateral automated negotiation tournament setting against past competitors. We show that our automatically configured agent outperforms all other agents, with a 5.1% increase in negotiation payoff compared to the next-best agent. We note that without our agent in the tournament, the top-ranked agent wins by a margin of only 0.01%.

Automated Configuration of Negotiation Strategies

While reinforcement learning (RL) has helped artificial agents solve challenging tasks, high sample complexity is still a major concern. Inter-agent teaching -- endowing agents with the ability to respond to instructions from others -- has been responsible for many developments towards scaling up RL. RL agents that can leverage instructions can learn tasks significantly faster than agents that cannot take advantage of such instruction. That said, the inter-agent teaching paradigm presents many new challenges due to, among other factors, differences between the agents involved in the teaching interaction. This paper is a summary of our JAAMAS article, where we propose two frameworks that provide a comprehensive view of the challenges associated with inter-agent teaching. We highlight state-of-the-art solutions, open problems, prospective applications, and argue that new research in this area should be developed in the context of the proposed frameworks.


Agents Teaching Agents: A Survey on Inter-agent Transfer Learning

Opportunism is a behavior that takes advantage of knowledge asymmetry and results in promoting agents' own value and demoting others' value. It is important to eliminate such selfish behavior in multi-agent systems, as it has undesirable results for the participating agents. In order for monitoring and eliminating mechanisms to be put in the right place, it is needed to know in which context agents are likely to perform opportunistic behavior. In this paper, we develop a formal framework to reason about agents' opportunistic propensity. Opportunistic propensity refers to the potential for an agent to perform opportunistic behavior. In particular, agents in the system are assumed to have their own value systems and knowledge. With value systems, we define agents' state preferences. Based on their value systems and incomplete knowledge about the state, they choose one of their rational alternatives to perform, which might be opportunistic behavior. We then characterize the situation where agents are likely to perform opportunistic behavior and the contexts where opportunism is impossible to occur, and prove the computational complexity of predicting opportunism.


A Formal Framework for Reasoning about Opportunistic Propensity in Multi-agent Systems

We study a sequential preference aggregation procedure based on voting rules for settings where several agents express their preferences over a common set of variable assignments via soft constraints. We evaluate this approach by providing both theoretical and experimental results. 


Sequential Voting in Multi-agent Soft Constraint Aggregation

Many real-world decision problems are inherently multi-objective in nature and concern multiple actors, making multi-objective multi-agent systems a key domain to study. We argue that trade-offs between conflicting objective functions should be analysed on the basis of the utility that these trade-offs have for the users of a system. We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures and utility functions. We analyse which solution concepts apply to the different settings in our taxonomy, which allows us to offer a structured view of the field and identify promising directions for future research.


Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey

Multi-objective multi-agent decision making: a utility based analysis and survey

The literature on norm emergence and normative MAS considers norms from two perspectives, namely: prescriptive norms using deontic concepts, and emergent norms that capture preference behaviour. We find that both perspectives lend themselves naturally to specific ways of representing norms in a society, as either explicit or implicit. 
Our analysis of the norm emergence literature, contributes several insights for future research in normative MAS. For example, opportunities for the study of online norm synthesis mechanisms and the investigation of the conversion of social norms, typically observed in norm emergence, to legal norms in normative MAS.  
Conversely, concepts from normative MAS can be brought into norm emergence research. For example, the study of stable emergence to avoid instability in MAS and the investigation of whether high cognitive ability agents, typically found in normative MAS, can demonstrate norm emergence. 
We summarise our analysis and outline future challenges and opportunities for cross-over between norm-emergence and normative systems research.


Norm Emergence in Multiagent Systems: A Viewpoint Paper

This presentation presents the Multi-Agent based Real-Time INfrastructure for Energy (MARTINE). MARTINE is a simulation and emulation infrastructure for the study of power and energy systems using a combination of artificial intelligence approaches. MARTINE combines real buildings with sensoring and actuation capabilities with real-time simulation, emulation of physical resources and the intelligent decision support to players actions. The infrastructure is managed and operated by means of several multi-agent systems, which connect to physical resources but also represent additional simulated players that are not present physically in the simulation and emulation environment.


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