The development of discursive knowledge presumes the communication of meaning as analytically different from the communication of information. Knowledge can then be considered as a meaning which makes a difference. Whereas the communication of information is studied in the information sciences and scientometrics, the communication of meaning has been central to Luhmann's attempts to make the theory of autopoiesis relevant for sociology. Analytical techniques such as semantic maps and the simulation of anticipatory systems enable us to operationalize the distinctions which Luhmann proposed as relevant to the elaboration of Husserl's "horizons of meaning" in empirical research: interactions among communications, the organization of meaning in instantiations, and the self-organization of interhuman communication in terms of symbolically generalized media such as truth, love, and power. Horizons of meaning, however, remain uncertain orders of expectations, and one should caution against reification from the meta-biological perspective of systems theory.

The state of the art in local search for the Traveling Salesman Problem is dominated by ejection chain methods utilising the Stem-and-Cycle reference structure. Though effective such algorithms employ very little information in their successor selection strategy, typically seeking only to minimise the cost of a move. We propose an alternative approach inspired from the AI literature and show how an admissible heuristic can be used to guide successor selection. We undertake an empirical analysis and demonstrate that this technique often produces better results than less informed strategies albeit at the cost of running in higher polynomial time.

We describe Cobot, a mixed initiative socio-semantic conversational search and recommendation system for finding health information. With Cobot, users can start a real time conversation about their health concerns. Cobot then connects relevant users together in the conversation also providing contextual recommendations relevant to the conversation. Conventional search engines and content portals provide a solitary search experience inundating the health information seeker with a hoard of information often confusing and frustrating them. Cobot brings relevant healthcare information directly or through other users without any search through natural language conversation.

Thus, design knowledge and a description of the desired product existing product environmental impact assessment to automatically synthesize potential solutions. This work approaches are most beneficial to implementing changes focuses on a morphological matrix based approach that during the detailed design phase. In addition, impacts due operates on information stored in a design repository to to materials choices, manufacturing processes utilized, and output high-level descriptions of possible solutions. The transportation of an existing product can be evaluated and following section describes the data source and concept reduced. It has been recognized, however, that generation algorithm.

In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. Like the original, it involves responding to typed English sentences, and English-speaking adults will have no difficulty with it. Unlike the original, the subject is not required to engage in a conversation and fool an interrogator into believing she is dealing with a person. Moreover, the test is arranged in such a way that having full access to a large corpus of English text might not help much. Finally, the interrogator or a third party will be able to decide unambiguously after a few minutes whether or not a subject has passed the test.

Buildings designed around occupant A sustainable home is more than a green building: it is also intelligence will provide flexible, adaptive task a living experience that encourages occupants to use fewer environments, refined control zones and technologies that resources more effectively. Research has shown that small maximize occupants' access to adaptive opportunities changes in behaviour in how we use our homes, such as (Cole & Brown, 2009). Architects, engineers and system turning off lights, reducing heat and uncovering or designers are faced with the challenge of reframing design covering windows, or shortening showers, can result in strategies as a co-evolution of human and building substantial energy and water savings. But changing the intelligence that will encourage as well as underpin way we use resources is proving challenging.

Organizational agility is a key challenge in today's business world. The Knowledge-Intensive Service Support approach tackles agility by combining process modeling and business rules. In the paper at hand, we present five approaches of process mining that could further increase the agility of processes by improving an existing process model.

Commonsense is a challenge not only for representation and reasoning but also for large scale knowledge engineering required to capture the breadth of our "everyday" world. One approach to knowledge engineering is to "outsource" the effort to the public through games that generate structured commonsense knowledge from user play. To date, such games have focused on symbolic and textual knowledge. However, an effective commonsense reasoning system will require spatial and physical reasoning capabilities. In this paper, I propose a tool for gathering commonsense information from ordinary people. It is a user-friendly 3D sculpting tool for modeling and annotating models of physical objects and spaces.

While much health data is available online, patients who are not technically astute may be unable to access it because they may not know the relevant resources, they may be reluctant to confront an unfamiliar interface, and they may not know how to compose an answer from information provided by multiple heterogeneous resources. We describe ongoing research in using natural English text queries and automated deduction to obtain answers based on multiple structured data sources in a specific subject domain. Each English query is transformed using natural language technology into an unambiguous logical form; this is submitted to a theorem prover that operates over an axiomatic theory of the subject domain. Symbols in the theory are linked to relations in external databases known to the system. An answer is obtained from the proof, along with an English language explanation of how the answer was obtained. Answers need not be present explicitly in any of the databases, but rather may be deduced or computed from the information they provide. Although English is highly ambiguous, the natural language technology is informed by subject domain knowledge, so that readings of the query that are syntactically plausible but semantically impossible are discarded. When a question is still ambiguous, the system can interrogate the patient to determine what meaning was intended. Additional queries can clarify earlier ones or ask questions referring to previously computed answers. We describe a prototype system, Quadri, which answers questions about HIV treatment using the Stanford HIV Drug Resistance Database and other resources. Natural language processing is provided by PARC’s Bridge, and the deductive mechanism is SRI’s SNARK theorem prover. We discuss some of the problems that must be faced to make this approach work, and some of our solutions.

This paper presents the formalization of a biological process, cell division, in modular action language ALM. We show how the features of ALM — modularity, separation between an uninterpreted theory and its interpretation — lead to a simple and elegant solution that can be used in answering questions from biology textbooks.