Belief-Desire-Intention (BDI) is a framework for modelling agents based on their beliefs, desires, and intentions. Plans are a central component of BDI agents, and define sequences of actions that an agent must undertake to achieve a certain goal. Existing approaches to plan generation often require significant manual effort, and are mainly focused on single-agent systems. As a result, in this work, we have developed a tool that automatically generates BDI plans using Alternating-Time Temporal Logic (ATL). By using ATL, the plans generated accommodate for possible competition or cooperation between the agents in the system. We demonstrate the effectiveness of the tool by generating plans for an illustrative game that requires agent collaboration to achieve a shared goal. We show that the generated plans allow the agents to successfully attain this goal.

Human relationships are complex processes that often involve following certain rules that regulate interactions and/or expected outcomes. These rules may be imposed by an authority or established by society. In multi-agent systems, normative systems have extensively addressed aspects such as norm synthesis, norm conflict detection, as well as norm emergence. However, if human behaviour is to be adequately simulated, not only normative aspects but also emotional aspects have to be taken into account. In this paper, we propose a Jason agent architecture that incorporates norms and emotions in its reasoning process to determine which plan (actions) to execute. The proposal is evaluated through a scenario based on a social network, which allows us to analyse the benefits of using emotional normative agents to achieve simulations closer to real human world.

In this paper, we present e-Genia3 an extension of AgentSpeak to provide support to the development of empathic agents. The new extension modifies the agent's reasoning processes to select plans according to the analyzed event and the affective state and personality of the agent. In addition, our proposal allows a software agent to simulate the distinction between self and other agents through two different event appraisal processes: the empathic appraisal process, for eliciting emotions as a response to other agents emotions, and the regular affective appraisal process for other non-empathic affective events. The empathic regulation process adapts the elicited empathic emotion based on intrapersonal factors (e.g., the agent's personality and affective memory) and interpersonal characteristics of the agent (e.g., the affective link between the agents). The use of a memory of past events and their corresponding elicited emotions allows the maintaining of an affective link to support long-term empathic interaction between agents.

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 framework is illustrated by means of a widely used agent programming example, GoldMiners.

Research on agent communication languages has typically taken the speech acts paradigm as its starting point. Despite their manifest attractions, speech-act models of communication have several serious disadvantages as a foundation for communication in artificial agent systems. In particular, it has proved to be extremely difficult to give a satisfactory semantics to speech-act based agent communication languages. In part, the problem is that speech-act semantics typically make reference to the "mental states" of agents (their beliefs, desires, and intentions), and there is in general no way to attribute such attitudes to arbitrary computational agents. In addition, agent programming languages have only had their semantics formalised for abstract, stand-alone versions, neglecting aspects such as communication primitives. With respect to communication, implemented agent programming languages have tended to be rather ad hoc. This paper addresses both of these problems, by giving semantics to speech-act based messages received by an AgentSpeak agent. AgentSpeak is a logic-based agent programming language which incorporates the main features of the PRS model of reactive planning systems. The paper builds upon a structural operational semantics to AgentSpeak that we developed in previous work. The main contributions of this paper are as follows: an extension of our earlier work on the theoretical foundations of AgentSpeak interpreters; a computationally grounded semantics for (the core) performatives used in speech-act based agent communication languages; and a well-defined extension of AgentSpeak that supports agent communication.

Existing action calculi provide rich, declarative formalisms for reasoning about actions. BDI-based programming languages like AgentSpeak, on the other hand, are procedural and geared towards practical applications of cognitive agents. In this paper, we close the gap between these two lines of research by integrating action calculi and AgentSpeak programs. Specifically, we develop a new and purely declarative semantics for AgentSpeak, which paves the way for combining this language with any suitable action calculus in a strictly modular fashion. As the main technical result, we prove that the new declarative semantics is correct wrt. the standard operational semantics for AgentSpeak. This provides the basis for a modular integration of a BDI-based agent programming language with sophisticated methods for reasoning about actions.

Existing action calculi provide rich, declarative formalisms for reasoning about actions. BDI-based programming languages like AgentSpeak, on the other hand, are procedural and geared towards practical applications of cognitive agents. In this paper, we close the gap between these two lines of research by integrating action calculi and AgentSpeak programs. Specifically, we develop a new and purely declarative semantics for AgentSpeak, which paves the way for combining this language with any suitable action calculus in a strictly modular fashion. As the main technical result, we prove that the new declarative semantics is correct wrt. the standard operational semantics for AgentSpeak. This provides the basis for a modular integration of a BDI-based agent programming language with sophisticated methods for reasoning about actions.

Research on agent communication languages has typically taken the speech acts paradigm as its starting point. Despite their manifest attractions, speech-act models of communication have several serious disadvantages as a foundation for communication in artificial agent systems. In particular, it has proved to be extremely difficult to give a satisfactory semantics to speech-act based agent communication languages. In part, the problem is that speech-act semantics typically make reference to the "mental states" of agents (their beliefs, desires, and intentions), and there is in general no way to attribute such attitudes to arbitrary computational agents. In addition, agent programming languages have only had their semantics formalised for abstract, stand-alone versions, neglecting aspects such as communication primitives. With respect to communication, implemented agent programming languages have tended to be rather ad hoc. This paper addresses both of these problems, by giving semantics to speech-act based messages received by an AgentSpeak agent. AgentSpeak is a logic-based agent programming language which incorporates the main features of the PRS model of reactive planning systems. The paper builds upon a structural operational semantics to AgentSpeak that we developed in previous work. The main contributions of this paper are as follows: an extension of our earlier work on the theoretical foundations of AgentSpeak interpreters; a computationally grounded semantics for (the core) performatives used in speech-act based agent communication languages; and a well-defined extension of AgentSpeak that supports agent communication.