A Framework for Evaluating Early-Stage Human - of Marcus Hutter

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Results and Discussion Figure 3 presents the number of decisions required to solve the CHS-Soar model of a map colouring problem (11 variables and 23 constraints) for the test cases outlined in Table 3. As highlighted the hard-coded variable and value texture selection heuristics (case 2) result in a lower number of decisions (41% reduction) then when using a Decisions 200 150 100 50 0 Map Colouring (11 Variables, 23 Constraints) 173 102 81 86 Case 1 - Random Selection Case 2 - Hard Coded Hueristics Case 3 - Internal learned chunks Case 4 - External learned chunks Figure 3: Map Colouring-Decisions as a Function of Subgoaling/Learning Test Cases. random selection of texture measures (case 1). When subgoaling and learning were enabled (case not shown), CHS-Soar generated on average 60 chunks. Case 3 demonstrates improved problem solving using the 60 internally acquired learned rules as compared to the explicit use of hard-coded heuristics (case 2) resulting in a 21% reduction in decision cycles. For case 3 it was observed that CHS-Soar used both a combination and range of variable and value texture measure types and values to secure a solution. Case 4 demonstrates the domain independent problem solving performance of CHS-Soar chunks using 177 chunks externally learned from the JJS problem. Case 4 resulted in a 16% reduction in decision cycles over case 2 (hard-coded heuristics). Yet for case 4 we observe a 6% increase in decision cycles as compared case 3 (internal learned chunks). Figure 4 presents the number of decision cycles required to solve the CHS-Soar model of a 3x5 Job-Shop Schedule (JSS) problem (15 variables and 35 constraints) for the test cases outlined in Table 3. Similar to the map colouring problem instance, we can observe a reduction in decision cycles (29% lower) using hard coded heuristics (case 2) over the random selection (case 1). Subgoaling and learning (one training run) resulted in 177 internally learned chunks. We note a 23% reduction in decision cycles when the problem is solved using internally learned rules (case 3) as compared with the explicit use of hard coded heuristics (case 2). Case 4 illustrates the impact of using 60 externally learned rules acquired from the map colouring problem. Case 4 shows a 10% reduction in the number of decision cycles as compared to case 2 (hard coded). 11 Decisions 100 75 50 25 0 (3x5) Job-Shop Scheduling Problem (15 Variables, 35 Constraints) 98 70 54 63 Case 1 - Random Selection Case 2 - Hard Coded Hueristics Case 3 - Internal learned chunks Figure 4: JSS-Decisions as a Function of Subgoaling/Learning Test Cases. Case 4 - External learned chunks Experiment 2: Scalability The second experiment explored the performance of externally learned chunks as a function of problem complexity for the map colouring problem. Specifically, the 177 learned chunks from the job-shop scheduling problem (3x5) were introduced into progressively larger map colouring problems ranging in size from 7 variables (9 constraints) to 44 variables (103 constraints). Results and Discussion Figure 5 presents a comparison of decisions cycles for 3 selected test cases (see Table 3 as a function of problem complexity for the map colouring problem. For case 5, the 177 externally learned chunks from the 3x5 JJS problem were introduced to solve each MC problem instance. Decisions 400 300 200 100 0 Case 2 - Hard Coded Hueristics Case 3 - Internal learned chunks Case 4 - External learned chunks Map Colouring Problem 7 11 22 33 44 Problem Complexity (Number of Variables) Figure 5: Map Colouring-Comparison of Decisions versus Problem Complexity for Selected Test Cases. As illustrated for this specific problem series, the externally acquired learned rules demonstrate similar problem solving performance, to both the hard-coded heuristics and internally generated chunks as problem complexity increases.
Conclusions and Future Work While preliminary, work to date has demonstrated how a specific form of constraint-based reasoning (CHS) can be effectively introduced into a symbolic cognitive architecture (Soar) using a generalized set of 34 productions. This results in an integration of two important types of reasoning techniques, namely constraint propagation and rule chaining. We have demonstrated the ability of CHS-Soar to learn rules while solving one problem type (i.e., graph colouring) that can be successfully applied in solving another problem type (i.e., Job Shop Scheduling). CHS and specifically the use of texture measures allow us to transform a problem specific search space (based on variables and values) into a more generalized one based on abstracted texture measures which provides the environment to achieve domain independent learning. Future work will include an expanded portfolio of test case problems to further validate and better understand CHS- Soar ability to learn and use domain independent texture based rules for more practical problems. More insightful texture measures, support augmentation (i.e. frequency) and improved texture evaluation functions are also being investigated as well as a texture based “discovery system.” References Bittle, S.A., Fox, M.S., (2008), "Introducing Constrained Heuristic Search to the Soar Cognitive Architecture", Technical Report, Enterprise Integration Laboratory http://www.eil.utoronto.ca/other/papers/index.html. Diaper, D., Huyck, C., Amavasai, B., Cheng, X. and Oussalah, M. 2007. Intelligently Engineering Artificial Intelligence Engineering: The Cognitive Architectures Competition. Proceedings of the workshop W3 on Evaluating Architectures for Intelligence, at the Association for the Advancement of Artificial Intelligence annual conference (Vancouver). Epstein, S. L., Freuder, E.C., Wallace, R.J, Morozov, A., Samuels, B. 2002. The Adaptive Constraint Engine. In P. Van Hentenryck, editor, Principles and Practice of Constraint Programming -- CP 2002: 8th International Conference, Proceedings, volume LNCS 2470 of Lecture Notes in Computer Science, pages 525--540. SpringerVerlag, 2002. Fox, M.S., Sadeh, N., Bayken, C., 1989. Constrained Heuristic Search. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pg:309– 315. Fox, M.S., 1986. Observations on the Role of Constraints in Problem Solving, Proceedings Sixth Canadian Conference on Artificial Intelligence, Montreal, Quebec. 12 Kumar, V., 1992. Algorithms for constraint-satisfaction problems: A survey. AI Magazine, 13(1):32-44, 1992 Laird, J.E., Newell, A., Rosenbloom, P. 1987. Soar: An Architecture for General Intelligence. Artificial Intelligence, 33: 1-64. Laird, J. E. 2008. Extending the Soar Cognitive Architecture. Artificial General Intelligence Conference, Memphis, TN. Langley, P., Laird, J., Rogers, S., 2006. Cognitive Architectures: Research Issues and Challenges, Technical Report, Computational Learning Laboratory, CSLI, Stanford University, CA.. Liu, B., 1994. Integrating Rules and Constraints Proceedings of the 6th IEEE International Conference on Tools with Artificial Intelligence (TAI-94), November 6-9, 1994, New Orleans, United States, 1994. Mackworth, A.K., 1977. Consistency in Networks of Relations, J. Artificial Intelligence, vol. 8, no. 1, pp. 99- 118, 1977. Pang, W., Goodwin, S.D., 2004. Application of CSP Algorithms to Job Shop Scheduling Problems, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5 0.9563. Shivers, O., 1986. Constraint propagation and macrocompilation in Soar. In Proceedings of the Soar Fall ‘86 Workshop, November 22, 1986. Steier, D.M., Newell, A., Flynn, R., Polk, T.A., Unruh, A., 1987. “Varieties of Learning in Soar.” The Soar Papers: Research on Integrated Intelligence, Volume 1, Chapter 18, Pages 536-548, MIT Press, Cambridge, Massachusetts. Subramanian, S.; Freuder, E.C. 1990. Rule compilation from constraint-based problem solving. Tools for Artificial Intelligence, 1990, Proceedings of the 2nd International IEEE Conference on, Vol., Iss, 6-9 Nov 1990, pp: 38-47. Swart, E.R., 1980. "The philosophical implications of the four-color problem". American Mathematical Monthly (JSTOR) 87 (9): 697--702. http://www.joma.org/images/ upload_library/22 /Ford/Swart697-707.pdf. Wallace, M., 1996. Practical applications of constraint programming, Constraints, An International Journal, 1, 139-168.
Page 2 and 3: Ben Goertzel, Pascal Hitzler, Marcu
Page 4 and 5: Artificial General Intelligence Vol
Page 6: Preface Artificial General Intellig
Page 9 and 10: Organizing Committee Tsvi Achler U.
Page 11 and 12: A formal framework for the symbol g
Page 13 and 14: Importing Space-time Concepts Into
Page 15 and 16: one structure, everything else adap
Page 17 and 18: generalize is essential to understa
Page 19 and 20: First Application The above framewo
Page 21 and 22: problem solving, use of knowledge,
Page 23: Of particular note is the separatio
Page 27 and 28: Conceptual Spaces The conceptual ar
Page 29 and 30: perceptions and actions. The lingui
Page 31 and 32: means of the knowledge stored in th
Page 33 and 34: grams given some (syntactically res
Page 35 and 36: For example, let reverse([]) = [] r
Page 37 and 38: Igor2 produced solutions with auxil
Page 39 and 40: evolve neural net modules as quickl
Page 41 and 42: ain”, consisting of some dozen or
Page 43 and 44: Population Chr NListPtr m Chrm is a
Page 45 and 46: and shaping, we feel that exploring
Page 47 and 48: Table 2 reviews the key capabilitie
Page 49 and 50: One way to achieve this goal would
Page 51 and 52: question. Our approach of staying a
Page 53 and 54: H0 δB(H0) δF(H0) H1 δB(H1) δF(H
Page 55 and 56: Discussion and future work Testing
Page 57 and 58: Types of Reasoning Corresponding Fo
Page 59 and 60: Integrating Different Types of Reas
Page 61 and 62: good overview of analogy models can
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Page 65 and 66: � ��
Page 67 and 68: ��
Page 69 and 70: A Unified Framework for IP Conditio
Page 71 and 72: negative evidence when added to a r
Page 73 and 74: ing strategy. Due to GOLEM’s rand
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any state index, n∈IN the current
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is the best observation summary, wh
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to a code for the rewards only, whi
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have exponentially many states (2O(
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where ˆσ 2 =Loss( ˆw)/n. Given
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• The cost function can be improv
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decides to introduce inflation or d
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€ € The diffusion matrix is the
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tuning may be done adaptively by te
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Of course, Harnad’s argument is n
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By exploring variations of Harnad
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Discussion The representational sys
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the cognitive map is less of a map
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models of cognition except in cases
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7(b), we can see that the straight
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eaction times, error rates, or exac
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comparing behavior with and without
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Doorenbos, R. B. 1994. Combining le
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egion, we leave the type of object
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extract features in support of reco
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with a minimum score of -1.0. The
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the NASA Human Error Modeling compa
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whether their models are capable of
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Incorporating Planning and Reasonin
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see an unclaimed ten-dollar bill al
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swering user queries) and the state
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Program Representation for General
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distribution P corresponding to the
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with all Ei replaced by the unbound
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Consciousness in Human and Machine:
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at the sensory receptors, is pre-pr
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The second choice is to take a deta
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Hebbian Constraint on the Resolutio
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The Case of Inputs that Change by T
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This route is relevant if the noise
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1. Oculus Info. Inc. Toronto, Ont.
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Our implemented Turing judge used a
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2.7; Human vs. JabberWacky t(157.6)
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UnsupervisedSegmentationofAudioSpee
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FinallyweusedthediscreteFourierTran
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Secondly,thereexistsmorethanonelogi
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Parsing PCFG within a General Proba
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known in advance, although many imp
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Figure 2: Shows the underlying weig
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Self-Programming: Operationalizing
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expressivity. More over, self-progr
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existence of a distance function ov
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Bootstrap Dialog: A Conversational
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elation is typically a verb. In the
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node RDF proposition N1 N2 N3 N4 N5
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Analytical Inductive Programming as
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Problem domain: puttable(x) PRE: cl
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eq Hanoi(0, Src, Aux, Dst, S) = mov
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Human and Machine Understanding Of
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case orderings t correspond to the
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in (1) received a different denotat
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Abstract This paper analyzes the di
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Having grounded meaning: In an inte
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to experience” can be argued to b
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Abstract Case-by-case Problem Solvi
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First, since NARS is designed in th
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typical response is to find such an
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What Is Artificial General Intellig
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Finally, the facts that both seem t
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differ. NARS uses Narsese, the fair
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Integrating Action and Reasoning th
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states, with the condition that the
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action model. The system is able to
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Neuroscience and AI Share the Same
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Relevance Based Planning: Why Its a
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General Intelligence and Hypercompu
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To appear, AGI-09 1 Stimulus proces
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Distribution of Environments in For
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The Importance of Being Neural-Symb
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Improving the Believability of Non-
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Understanding the Brain’s Emergen
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Abstract The challenge of creating
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Importing Space-time Concepts Into
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HELEN: Using Brain Regions and Mech
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Holistic Intelligence: Transversal
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Achieving Artificial General Intell
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Achler, Tsvi . . . . . . . . . . .
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