Programming Cognitive Agents in Defeasible Logic

Defeasible Logic is extended to programming languages for cognitive agents with preferences and actions for planning. We define rule-based agent theories that contain preferences and actions, together with inference procedures. We discuss patterns of agent types in this setting. Finally, we illustrate the language by an example of an agent reasoning about web-services

Interaction between Normative Systems and Cognitive Agents in Temporal Modal Defeasible Logic

While some recent frameworks on cognitive agents addressed the combination of mental attitudes with deontic concepts, they commonly ignore the representation of time. An exception is [1]that manages also some temporal aspects both with respect to cognition and normative provisions. We propose in this paper an extension of the logic presented in [1]with temporal intervals

Efficient representation and effective reasoning for multi-agent systems

In multi-agent systems, interactions between agents are often related to cooperation or competition in such a fashion that they can fulfil their tasks. Successful interactions often require agents to share common and unified knowledge about their working environment. However, autonomous agents observe and judge their surroundings by their own view. Consequently, agents possibly have partial and sometimes conflicting descriptions of the world. In scenarios where they have to coordinate, they are required to identify the shared knowledge in the group and to be able to reason with available information. This problem requires more sophisticated modelling and reasoning methods, which is beyond the classical logics and monotonic reasoning. We introduce a formal framework based on Defeasible Logic (DL) to describe the knowledge commonly shared by agents, and that obtained from other agents. This enables an agent to efficiently reason about the environment and intentions of other agents given available information. We propose to extend the reasoning mechanism of DL with the superior knowledge. This mechanism allows an agent to integrate its mental attitude with a more trustworthy source of information such as the knowledge shared by the majority of other agents

Strategic argumentation: A game theoretical investigation

Argumentation is modelled as a game where the payoffs are measured in terms of the probability that the claimed conclusion is, or is not, defeasibly provable, given a history of arguments that have actually been exchanged, and given the probability of the factual premises. The probability of a conclusion is calculated using a standard variant of Defeasible Logic, in combination with standard probability calculus. It is a new element of the present approach that the exchange of arguments is analysed with game theoretical tools, yielding a prescriptive and to some extent even predictive account of the actual course of play. A brief comparison with existing argument-based dialogue approaches confirms that such a prescriptive account of the actual argumentation has been almost lacking in the approaches proposed so far

Contextual Deliberation of Cognitive Agents in Defeasible Logic

This article extends Defeasible Logic to deal with the contextual deliberation process of cognitive agents. First, we introduce meta-rules to reason with rules. Meta-rules are rules that have as a consequent rules for motivational components, such as obligations, intentions and desires. In other words, they include nested rules. Second, we introduce explicit preferences among rules. They deal with complex structures where nested rules can be involved

Agents adapt to majority behaviours

Agents within a group can have different perceptions of their working environment and autonomously fulfil their goals. However, they can be aware of beliefs and goals of the group as well as other members so that they can adjust their behaviours accordingly. To model these agents, we explicitly include knowledge commonly shared by the group and that obtained from other agents. By avoiding actions which violate ``mental attitudes'' shared by the majority of the group, agents demonstrate their social commitment to the group. Defeasible logic is chosen as our representation formalism for its computational efficiency, and for its ability to handle incomplete and conflicting information. Hence, our agents can enjoy the low computational cost while performing ``reasoning about others''. Finally, we present the implementation of our multi-agent system

Preferences of Agents in Defeasible Logic

We are interested in programming languages for cognitive agents with preferences. We define rule-based agent theories and inference procedures in defeasible logic, and in this setting we discuss patterns of agent behavior called agent types

Contextual Agent Deliberation in Defeasible Logic

This article extends Defeasible Logic to deal with the contextual deliberation process of cognitive agents. First, we introduce meta-rules to reason with rules. Meta-rules are rules that have as a consequent rules for motivational components, such as obligations, intentions and desires. In other words, they include nested rules. Second, we introduce explicit preferences among rules. They deal with complex structures where nested rules can be involved

Updating Action Descriptions and Plans for Cognitive Agents

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The Cost of Social Agents

In this paper we follow the BOID (Belief, Obligation, Intention, Desire) architecture to describe agents and agent types in Defeasible Logic. We argue that the introduction of obligations can provide a new reading of the concepts of intention and intentionality. Then we examine the notion of social agent (i.e., an agent where obligations prevail over intentions) and discuss some computational and philosophical issues related to it. We show that the notion of social agent either requires more complex computations or has some philosophical drawbacks