Todorka Glushkova (October 21st 2015). Personalization and User Modeling in Adaptive E-Learning Systems for Schools, E-Learning - Instructional Design, Organizational Strategy and Management, Boyka Gradinarova, IntechOpen, DOI: 10.5772/61084.

Ammar Mohammed, Ulrich Furbach and Frieder Stolzenburg (January 1st 2010). Multi-Robot Systems: Modeling, Specification, and Model Checking, Robot Soccer, Vladan Papi&#263;, IntechOpen, DOI: 10.5772/7349.

Michal Ptaszynski, Pawel Dybala, Shinsuke Higuhi, Wenhan Shi, Rafal Rzepka and Kenji Araki (March 1st 2010). Towards Socialized Machines: Emotions and Sense of Humour in Conversational Agents, Web Intelligence and Intelligent Agents, Zeeshan-Ul-Hassan Usmani, IntechOpen, DOI: 10.5772/8384. 

New Zealand

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.

AAMAS 2020

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

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

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). 

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.


MARTINE: Multi-Agent based Real-Time INfrastructure for Energy

When robots act in an environment, there will be temporal uncertainty over the execution of their actions, i.e. the duration of an action and the time it takes place will be stochastic. The presence of multiple robots in the environment contributes towards this uncertainty.  Temporal uncertainty is often disregarded in multi-robot coordination, and so in this presentation, I will discuss why it is important to model temporal uncertainty, and detail the benefits of doing so. Following this, I will outline my current work modelling temporal uncertainty in terms of congestion in order to generate effective plans for multi-robot navigation tasks. Finally, I will discuss the requirements to achieve general multi-robot coordination under temporal uncertainty.


Towards Multi-Robot Coordination under Temporal Uncertainty (Doctoral Consortium)

This paper presents part of the work developed so far within the scope of my PhD and suggests possible future research directions. My thesis tackles the problem of multi-robot coordination under uncertainty over the long-term. We present a preliminary approach that tackles multi-robot monitoring problems under uncertain task durations. We propose a methodology that takes advantage of a modeling formalism for robot teams: generalized stochastic Petri nets with rewards (GSPNR). A GSPNR allows for unified modeling of action selection and uncertainty on duration of action execution. At the same time, it allows for goal specification through the use of transition rewards and rewards per time unit. The proposed approach exploits the well-defined semantics provided by Markov reward automata in order to synthesize policies.


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

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

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Automated Configuration of Negotiation Strategies

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Code-switched word embeddings for sentiment analysis

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