Group Decision Making and Temporal Reasoning

The more capable and autonomous computer systems become, the more important it is for them to be able to act collaboratively, whether in groups consisting solely of other computers or in heterogeneous groups of computers and people. To act collaboratively requires that systems have effective group decision-making capabilities. This thesis makes four important contributions to the design of group decision-making mechanisms and algorithms for deploying them in collaborative, multi-agent systems. First, it provides an abstract framework for the specification of group decision-making mechanisms that computer agents can use to coordinate their planning activity when collaborating with other agents. Second, it specifies a combinatorial auction-based mechanism that computer agents can use to help them decide, both individually and collectively, whether to engage in a collaborative activity. Third, it extends the theory of Simple Temporal Networks by providing a rigorous theoretical analysis of an important family of temporal reasoning problems. Fourth, it provides sound, complete and polynomial-time algorithms for solving those temporal reasoning problems and specifies the use of such algorithms by agents participating in the auction-based mechanism.Engineering and Applied Science

Systematic composition of distributed objects: Processes and sessions

We consider a system with the infrastructure for the creation and interconnection of large numbers of distributed persistent objects. This system is exemplified by the Internet: potentially, every appliance and document on the Internet has both persistent state and the ability to interact with large numbers of other appliances and documents on the Internet. This paper elucidates the characteristics of such a system, and proposes the compositional requirements of its corresponding infrastructure. We explore the problems of specifying, composing, reasoning about and implementing applications in such a system. A specific concern of our research is developing the infrastructure to support structuring distributed applications by using sequential, choice and parallel composition, in the anarchic environment where application compositions may be unforeseeable and interactions may be unknown prior to actually occurring. The structuring concepts discussed are relevant to a wide range of distributed applications; our implementation is illustrated with collaborative Java processes interacting over the Internet, but the methodology provided can be applied independent of specific platforms

Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

An empirical study of argumentation schemes in deliberative dialogue

Publisher PD

Non-collaborative Attackers and How and Where to Defend Flawed Security Protocols (Extended Version)

Security protocols are often found to be flawed after their deployment. We present an approach that aims at the neutralization or mitigation of the attacks to flawed protocols: it avoids the complete dismissal of the interested protocol and allows honest agents to continue to use it until a corrected version is released. Our approach is based on the knowledge of the network topology, which we model as a graph, and on the consequent possibility of creating an interference to an ongoing attack of a Dolev-Yao attacker, by means of non-collaboration actuated by ad-hoc benign attackers that play the role of network guardians. Such guardians, positioned in strategical points of the network, have the task of monitoring the messages in transit and discovering at runtime, through particular types of inference, whether an attack is ongoing, interrupting the run of the protocol in the positive case. We study not only how but also where we can attempt to defend flawed security protocols: we investigate the different network topologies that make security protocol defense feasible and illustrate our approach by means of concrete examples.Comment: 29 page

Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference