Evaluation of Multi-Agent Systems: Proposal and Validation of a Metric Plan

Bitonto, Pierpaolo Di; Laterza, Maria; Roselli, Teresa; Rossano, Veronica

doi:10.1007/978-3-642-32066-8_9

Cited by 5 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the valuable insights this work unveils, it presents a conceptual LEC model, supported by a set of assumptions informed by the existing literature on energy communities and the authors experience on smart grids as well as behavioral and tech-nical demand response research and practice. As the LEC that this work aims to model has not been materialized, there is no real basis for comparison that allows to discuss how far the model's outcomes are to represent the behavior of the target system (correctness) or to what extent the conceptual framework of the model and the target system match (consistency) [76]. Therefore, results must be understood as experimental and exploratory.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Influence of Different Goals in Energy Communities’ Self-Sufficiency—An Optimized Multiagent Approach

et al. 2021

View full text Add to dashboard Cite

Understanding to what extent the emergence of prosumers and prosumagers organized in energy communities can impact the organization and operation of power grids has been one of the major recent research avenues at the European level. In renewable-based communities aiming to reach some level of energy self-sufficiency, a key issue to be addressed is assessing how the presence of end-users playing different roles in the system (self-consuming, producing and trading, performing demand management, etc.) can influence the overall system performance. In this setting, this paper combines Distributed Artificial Intelligence and optimization approaches to assess how prosumagers and consumers pursuing different goals can influence the energy self-sufficiency of a local energy community. The residential demand is accurately modeled, and the agents’ preferences are considered in the modeling to represent a smart community. The results show that although energy community members may have conflicting individual goals, the overall system self-sufficiency can be maximized with economic benefits for all stakeholders, thus illustrating the advantages of energy communities.

show abstract

Section: Discussionmentioning

confidence: 99%

Assessing the Influence of Different Goals in Energy Communities’ Self-Sufficiency—An Optimized Multiagent Approach

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Few works are devoted to the evaluation perspective, and when it is the case, they are focussed on evaluating some characteristics of the agents such as their rationality, autonomy or reactivity (see [11] for an example). However, in order to evaluate social intelligence, it is convenient to put efforts not only on the agents, but also on the environments where they have to interact, treating the system as a whole.…”

Section: Machinesmentioning

confidence: 99%

“…To find General max (equation 8.2), we need to find a pair ⟨Π e , Π o ⟩ that maximises the property value as much as possible. We can have this situation by selecting Π e = {π s1 , π s2 , π s3 } and Π o = {π x } (a π s agent always stays in the same cell 11 , and a π x agent acts stochastically with a probability of 1/ √ 2 to do not reach the upper left corner and a probability of 1 − 1/ √ 2 to reach this corner).…”

Section: C5 Partial Gradingmentioning

confidence: 99%

Towards a Universal Test of Social Intelligence

Cabrera¹

View full text Add to dashboard Cite

Under the view of artificial intelligence, an intelligent agent is an autonomous entity which interacts in an environment through observations and actions, trying to achieve one or more goals with the aid of several signals called rewards. The creation of intelligent agents is proliferating during the last decades, and the evaluation of their intelligence is a fundamental issue for their understanding, construction and improvement.Social intelligence is recently obtaining special attention in the creation of intelligent agents due to the current view of human intelligence as highly social. Social intelligence in natural and artificial systems is usually measured by the evaluation of associated traits or tasks that are deemed to represent some facets of social behaviour. The amalgamation of these traits or tasks is then used to configure an operative notion of social intelligence. However, this operative notion does not truly represent what social intelligence is and a definition following this principle will not be precise. Instead, in this thesis we investigate the evaluation of social intelligence in a more formal and general way, by actually considering the evaluee's interaction with other agents.In this thesis we analyse the implications of evaluating social intelligence using a test that evaluates general intelligence. For this purpose, we include other agents into an initially singleagent environment to figure out the issues that appear when evaluating an agent in the context of other agents. From this analysis we obtain useful information for the evaluation of social intelligence.From the lessons learned, we identify the components that should be considered in order to measure social intelligence, and we provide a formal and parametrised definition of social intelligence. This definition calculates an agent's social intelligence as its expected performance in a set of environments with a set of other agents arranged in teams and participating in lineups, with rewards being re-understood appropriately. This is conceived as a tool to define social intelligence testbeds where we can generate several degrees of competitive and cooperative behaviours. We test this definition by experimentally analysing the influence of teams and agent line-ups for several multi-agent systems with variants of Q-learning agents.However, not all testbeds are appropriate for the evaluation of social intelligence. To facilitate the analysis of a social intelligence testbed, we provide some formal property models about social intelligence in order to characterise the testbed and thus assess its suitability. Finally, we use the presented properties to characterise some social games and multi-agent environments, we make a comparison between them and discuss their strengths and weaknesses in order to evaluate social intelligence.ii ResumenBajo la visión de la inteligencia artificial, un agente inteligente es una entidad autónoma la cual interactúa en un entorno a través de observaciones y acciones, tratando de lograr uno o más ...

show abstract