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Learning and Evolving Agents in User Monitoring and Training
"... Abstract. We propose a general vision for agents in Ambient Intelligent applications, whereby agents monitor and un-intrusively train human users, and learn their patterns of behavior not only by observing and generalizing their observations, but also by “imitating ” them. Agents can also learn by “ ..."
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Abstract. We propose a general vision for agents in Ambient Intelligent applications, whereby agents monitor and un-intrusively train human users, and learn their patterns of behavior not only by observing and generalizing their observations, but also by “imitating ” them. Agents can also learn by “imitating ” other agents, when being told by them. Within this vision, agents need to evolve to take into account what they learn from or about users, and as a result of monitoring the users. 1
Defining and Maintaining Agent’s Experience in Logical Agents
"... Abstract. In this paper, we extend our previous approach to memory in the DALI language from facts to (sets of) rules, and we extend their management by introducing operators for reasoning about the context and agent is involved in, and about modules that should be associated to that context in the ..."
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Abstract. In this paper, we extend our previous approach to memory in the DALI language from facts to (sets of) rules, and we extend their management by introducing operators for reasoning about the context and agent is involved in, and about modules that should be associated to that context in the working memory. We exploit and extend our past work where we introduced meta-axioms for run-time self-checking and self-reconfiguration and the possibility of employing sub-modules for various forms of reasoning. 1
Ensuring Agent Properties under Arbitrary Sequences of Incoming Events
"... This paper deals with run-time detection and possible correction of erroneous and/or anomalous behavior in agents. Agent behavior is affected by its interaction with the external world, i.e., by events perceived by the agent and in which order. Nevertheless, in most practical cases, the actual arriv ..."
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This paper deals with run-time detection and possible correction of erroneous and/or anomalous behavior in agents. Agent behavior is affected by its interaction with the external world, i.e., by events perceived by the agent and in which order. Nevertheless, in most practical cases, the actual arrival order of events is unforeseeable, and the set of possible events is so large that computing all combinations would result in a combinatorial explosion, resorting to “priori ” verification techniques is actually unpractical. However, properties that one wants to verify often depend upon which events have been observed by an agent up to a certain point, and which other ones are supposed to occur later. Therefore, we augment our previous approaches by allowing an agent to explicitly observe and record its past behavior so as to be able to decide its best actions, and avoid errors performed in previous similar situations. 1
Self-checking Logical Agents
"... Abstract. This paper presents a comprehensive framework for run-time selfchecking of logical agents, by means of temporal axioms to be dynamically checked. These axioms are specified by using an agent-oriented interval temporal logic defined to this purpose. We define syntax, semantics and pragmatic ..."
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Abstract. This paper presents a comprehensive framework for run-time selfchecking of logical agents, by means of temporal axioms to be dynamically checked. These axioms are specified by using an agent-oriented interval temporal logic defined to this purpose. We define syntax, semantics and pragmatics for this new logic, specifically tailored for application to agents. In the resulting framework, we encompass and extend our past work. 1
