Contributions to an anthropological approach to the cultural adaptation of migrant agents

This thesis proposes the use of Cultural Anthropology as a source of inspiration for solutions to the problem of adaptation of autonomous, intelligent, computational agents that migrate to societies of agents with distinctive features from the ones of the society where those agents were originally conceived. This has implications for interoperation of disparate Multi-Agent Systems. In particular, the cognitive approach to anthropology is argued to be a suitable theoretical foun-dation for this topic. Fieldwork practice in social anthropology is also indicated as an useful source of ideas. A pragmatic theory of intensionality is incorporated in this anthropological approach, resulting in a mechanism that allows agents to ascribe intensional ontologies of terms to societies that use unfamiliar means of communication; also, taxonomical relations among the terms in such ontologies can be retrieved, by means of a process inspired by the counterpart activity of ethnographers. This is presented using the Z notation for formal specification of systems, and illustrated on a set of terms from the game of cricket. Subsequently, a simulation of a game of cricket is described where one of the players is unfamiliar with the game, and therefore needs to learn the game by observing the other players. A reasonable behaviour for such a player is obtained, and the simulation offers grounds for further anthropologically-based studies. Further, a study of theories of moral sentiments is presented, and the Iterated Prisoner's Dilemma is used in simulations based on those ideas. The results of the simulations show clearly the positive impact, on groups of agents, of altruistic behaviour; this can only be coherently obtained in autonomous agents by modelling emotions, which are relevant for this project as anthropologists recognise them as an essential cross-cultural link. Finally, the consequences of this project to conceptions of Distributed Artificial Intelligence are discussed

A multi-agent system to manage users and spaces in a adaptive environment system

This paper, deals with the actual problem of manage user preferences and local specifications on an IoT adaptive system, namely using a multi agent system to achieve a Smart Environment System. On a new era of interaction between persons and physical spaces, users want those spaces smartly adapt to their preferences in a transparent way. To achieve that, new approaches are needed. In this project we develop a multi agent system architecture with different layers to achieve a solution that entails all the proposed objectives.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Using jason framework to develop a multi-agent system to manage users and spaces in an adaptive environment system

Manage user preferences and local specifications on an IoT adaptive system is a actual problem. This paper uses Jason framework to develop a multi agent system to achieve a Smart Environment System, and supports interaction between persons and physical spaces, that users want to smartly adapt to their preferences in a transparent way. This work proposes a new approach, that has been developed using a multi agent system architecture with different layers to achieve a solution that entails all the proposed objectives.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Probabilistic Perception Revision in AgentSpeak(L)

Agent programming is mostly a symbolic discipline and, as such, draws little benefits from probabilistic areas as machine learning and graphical models. However, the greatest objective of agent research is the achievement of autonomy in dynamical and complex environments — a goal that implies embracing uncertainty and therefore the entailed representations, algorithms and techniques. This paper proposes an innovative and conflict free two layer approach to agent programming that uses already established methods and tools from both symbolic and probabilistic artificial intelligence. Moreover, this method is illustrated by means of a widely used agent programming example, GOLDMINERS

Reasoning about the executability of goal-plan trees

User supplied domain control knowledge in the form of hierarchically structured agent plans is at the heart of a number of approaches to reasoning about action. This knowledge encodes the “standard operating procedures” of an agent for responding to environmental changes, thereby enabling fast and effective action selection. This paper develops mechanisms for reasoning about a set of hierarchical plans and goals, by deriving “summary information” from the conditions on the execution of the basic actions forming the “leaves” of the hierarchy. We provide definitions of necessary and contingent pre-, in-, and postconditions of goals and plans that are consistent with the conditions of the actions forming a plan. Our definitions extend previous work with an account of both deterministic and non-deterministic actions, and with support for specifying that actions and goals within a (single) plan can execute concurrently. Based on our new definitions, we also specify requirements that are useful in scheduling the execution of steps in a set of goal-plan trees. These requirements essentially define conditions that must be protected by any scheduler that interleaves the execution of steps from different goal-plan trees

SAJaS: enabling JADE-based simulations

Multi-agent systems (MAS) are widely acknowledged as an appropriate modelling paradigm for distributed and decentralized systems, where a (potentially large) number of agents interact in non-trivial ways. Such interactions are often modelled defining high-level interaction protocols. Open MAS typically benefit from a number of infrastructural components that enable agents to discover their peers at run-time. On the other hand, multi-agent-based simulations (MABS) focus on applying MAS to model complex social systems, typically involving a large agent population. Several MAS development frameworks exist, but they are often not appropriate for MABS; and several MABS frameworks exist, albeit sharing little with the former. While open agent-based applications benefit from adopting development and interaction standards, such as those proposed by FIPA, MABS frameworks typically do not support them. In this paper, a proposal to bridge the gap between MAS simulation and development is presented, including two components. The Simple API for JADE-based Simulations (SAJaS) enhances MABS frameworks with JADE-based features. While empowering MABS modellers with modelling concepts offered by JADE, SAJaS also promotes a quicker development of simulation models for JADE programmers. In fact, the same implementation can, with minor changes, be used as a large scale simulation or as a distributed JADE system. In its current version, SAJaS is used in tandem with the Repast simulation framework. The second component of our proposal consists of a MAS Simulation to Development (MASSim2Dev) tool, which allows the automatic conversion of a SAJaS-based simulation into a JADE MAS, and vice-versa. SAJaS provides, for certain kinds of applications, increased simulation performance. Validation tests demonstrate significant performance gains in using SAJaS with Repast when compared with JADE, and show that the usage of MASSim2Dev preserves the original functionality of the system. © Springer-Verlag Berlin Heidelberg 2015

CAMP-BDI: A Pre-emptive Approach for Plan Execution Robustness in Multiagent Systems

Abstract. Belief-Desire-Intention agents in realistic environments may face un-predictable exogenous changes threatening intended plans and debilitative failure effects that threaten reactive recovery. In this paper we present the CAMP-BDI (Capability Aware, Maintaining Plans) approach, where BDI agents utilize intro-spective reasoning to modify intended plans in avoidance of anticipated failure. We also describe an extension of this approach to the distributed case, using a de-centralized process driven by structured messaging. Our results show significant improvements in goal achievement over a reactive failure recovery mechanism in a stochastic environment with debilitative failure effects, and suggest CAMP-BDI offers a valuable complementary approach towards agent robustness.

Semantic Mutation Testing for Multi-Agent Systems

This paper introduces semantic mutation testing (SMT) into multiagent systems. SMT is a test assessment technique that makes changes to the interpretation of a program and then examines whether a given test set has the ability to detect each change to the original interpretation. These changes represent possible misunderstandings of how the program is interpreted. SMT is also a technique for assessing the robustness of a program to semantic changes. This paper applies SMT to three rule-based agent programming languages, namely Jason, GOAL and 2APL, provides several contexts in which SMT for these languages is useful, and proposes three sets of semantic mutation operators (i.e., rules to make semantic changes) for these languages respectively, and a set of semantic mutation operator classes for rule-based agent languages. This paper then shows, through preliminary evaluation of our semantic mutation operators for Jason, that SMT has some potential to assess tests and program robustness

Implementing Argumentation-enabled Empathic Agents

In a previous publication, we introduced the core concepts of empathic agents as agents that use a combination of utility-based and rule-based approaches to resolve conflicts when interacting with other agents in their environment. In this work, we implement proof-of-concept prototypes of empathic agents with the multi-agent systems development framework Jason and apply argumentation theory to extend the previously introduced concepts to account for inconsistencies between the beliefs of different agents. We then analyze the feasibility of different admissible set-based argumentation semantics to resolve these inconsistencies. As a result of the analysis we identify the maximal ideal extension as the most feasible argumentation semantics for the problem in focus.Comment: Accepted for/presented at the 16th European Conference on Multi-Agent Systems (EUMAS 2018

Incorporating social practices in BDI agent systems

When agents interact with humans, either through embodied agents or because they are embedded in a robot, it would be easy if they could use fixed interaction protocols as they do with other agents. However, people do not keep fixed protocols in their day-to-day interactions and the environments are often dynamic, making it impossible to use fixed protocols. Deliberating about interactions from fundamentals is not very scalable either, because in that case all possible reactions of a user have to be considered in the plans. In this paper we argue that social practices can be used as an inspiration for designing flexible and scalable interaction mechanisms that are also robust. However, using social practices requires extending the traditional BDI deliberation cycle to monitor landmark states and perform expected actions by leveraging existing plans. We define and implement this mechanism in Jason using a periodically run meta-deliberation plan, supported by a metainterpreter, and illustrate its use in a realistic scenario.Comment: An extended abstract of this paper has been accepted for the Eighteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 201